FTIR Analysis of Experimental Adhesives: Investigating Spectral Reproducibility, Chemometric Approaches, and Archaeological Applications

FTIR Analysis of Experimental Adhesives: Investigating Spectral Reproducibility, Chemometric Approaches, and Archaeological Applications | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article FTIR Analysis of Experimental Adhesives: Investigating Spectral Reproducibility, Chemometric Approaches, and Archaeological Applications Lauren Nicole Lien, Susan M Mentzer, Veerle Rots This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7711776/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Reflectance-mode Fourier transform infrared (FTIR) spectroscopy is increasingly employed in archaeological residue studies, offering a non-destructive means to investigate Paleolithic adhesive technologies. This study evaluates the reproducibility and comparability of reflectance-mode FTIR spectra collected from experimental adhesives on flint substrates, analyzed across an eight-year interval using two different FTIR instruments. A comprehensive suite of natural resins, gums, glues, and admixtures was assessed to examine spectral variability introduced by instrument configuration, sample orientation, and residue composition. To evaluate classification accuracy and interpretive consistency, both analyst-defined and ingredient-defined grouping strategies were applied to processed spectra. Chemometric methods including Principal Component Analysis (PCA) and Hierarchical Clustering Analysis (HCA) were used to investigate compositional trends and clustering, supplemented by a blind validation set of pure adhesives. While key chemical features were preserved across instruments after standardized processing, minor spectral differences introduced variability in chemometric clustering. In contrast, analyst-based groupings following a Kramers-Kronig transformation remained largely consistent across instruments and sample conditions. The results highlight the value of integrating visual inspection with chemometric tools and underscore the importance of tailored preprocessing strategies, transparent classification criteria and realistic experimental references. Reflectance-mode FTIR, when paired with reproducible workflows and robust interpretive strategies, offers a promising approach for identifying archaeological adhesive residues, particularly in contexts where destructive sampling is limited. FTIR residue adhesive lithics spectroscopy experimental archaeology Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Figure 13 Figure 14 Figure 15 Figure 16 1. Introduction The microscopic study of residues on lithic artifacts plays a critical role in archaeological reconstructions of ancient lifeways, offering rare and direct evidence of tool function, resource selection and use, technological behaviors, and complex cognitive processing skills (Williamson 2004 ; Mazza et al. 2006 ; Wadley 2010 ; Wragg Sykes 2015 ; Rots et al. 2016 ; Zupancich et al. 2016 ; Degano et al. 2019 ; Venditti et al. 2019 ; Kozowyk et al. 2023 ). Among the most informative of these residues are adhesives—particularly compound adhesives, those which consist of multiple ingredients—used in the hafting of composite tools (Aveling and Heron 1998 ; Rots 2008a ; Wadley et al. 2009 ; Wadley 2010 ; Zipkin et al. 2014 ; Rageot et al. 2019 ; Chasan et al. 2024 ). Such materials hold immense potential for understanding complex technological processes and the cognitive capacities of past populations (Wadley et al. 2009 ; Wadley 2010 ; Wragg Sykes 2015 ; Rots et al. 2017 ). However, identifying and interpreting such residues can be fraught with methodological challenges (Wadley and Lombard 2007; Rots 2008b ; Prinsloo et al. 2014 ; Rots et al. 2016 ; Nucara et al. 2020 ; Cnuts and Rots 2024 ). Issues of preservation (Langejans 2010 ; Zurro and Gadekar 2024 ), contamination (Croft et al. 2018 ; Pedergnana 2020 ; Cnuts et al. 2022 ; Frahm et al. 2022 ; Zurro and Gadekar 2024 ), and interpretive ambiguity (Crowther and Haslam 2007; Wadley and Lombard 2007; Pedergnana 2020 ; Zurro and Gadekar 2024 ) continue to limit the field’s ability to derive robust conclusions from the presence of residues alone. Within this context, Fourier transform infrared (FTIR) spectroscopy has become increasingly recognized as a valuable, non-destructive analytical technique capable of characterizing organic and inorganic compounds at a microscopic scale, with broad application across many sub-fields of archaeological science (e.g. Shillito et al. 2009 ; Weiner 2010 ; Nunziante-Cesaro and Lemorini 2012 ; Solodenko et al. 2015 ; Bradtmoller et al. 2016; Monnier et al. 2017a ; Monnier et al. 2017b ; Monnier 2018 ; Lemorini et al. 2020 ; Dominici et al. 2022 ; Mentzer 2023 ). Beginning in the mid-20th century and gaining steady traction since, FTIR has been a versatile tool in related disciplines. Adopted into geological studies in the 1950s, FTIR has enabled the identification and characterization of minerals and proved itself as a reliable method alongside traditional petrographic approaches (Keller and Pickett 1950 ; Launer 1952 ; Adler and Kerr, 1965 ; Farmer 1974 ). Applications of FTIR to the study of sediments in archaeological contexts were pioneered largely by Steve Weiner, who began using the technique to explore the relationship between bone preservation, secondary minderals and archaeological site formation processes (DeNiro and Weiner 1988 ; Weiner and Goldberg 1990 ; Weiner et al. 1993 ; Weiner et al. 2002 ). Other strong applications include sourcing and characterization of amber and chert in provenance studies and paleoenvironmental reconstruction (Galletti and Mazzeo 1993; Angelini and Bellintani 2005 ; Wolfe et al. 2009 ; Parish et al. 2013 ; Qu et al. 2015 ; Havelcova et al. 2016). Reflectance FTIR analyses, which can be conducted in a fully non-destructive manner depending on sample configuration, have also been applied to address similar research questions (e.g. mineral identification and site formation processes: Berna 2017 , Morrissey et al. 2025 ; and chert sourcing: Parish et al. 2013 , Parish 2016 , Schürch et al. 2022 ). More recently, FTIR has shown considerable promise in identifying a wide range of organic archaeological residues, including plant gums, resins, waxes, and proteinaceous materials (Cinta-Pinzaru et al. 2012 ; Nunziante-Cesaro and Lemorini 2012 ; Bruni and Guglielmi 2014 ; Martin-Ramos et al. 2018 ; Lemorini et al. 2020 ; Aleo et al. 2024 ). In particular, its use in reflectance mode is non-destructive, which allows for minimal sample preparation and the preservation of specimens for future analyses (Tappert et al. 2011 ; Beasley et al. 2014 ; Prinsloo et al. 2014 ; Monnier 2018 ; Lemorini et al. 2020 ). Despite these advantages, FTIR is still underutilized for residue analysis in archaeological contexts, in part because of a lack of methodological standardization (Nunziante-Cesaro and Lemorini 2012 ; Monnier 2018 ), gaps in the coverage of reference libraries (Prinsloo et al. 2014 ), and difficulties distinguishing residue signals from those of the underlying substrates or from other sources of interference such as post-depositional transformation and contamination (Monnier 2018 ; McAdams et al. 2021 ; Frahm et al. 2022 ). Without careful methodological controls, these signals can easily be misinterpreted, potentially skewing interpretations of tool use and behavior (Lopez-Ballester et al. 1999 ; Vahur et al. 2011 ; Cnuts et al. 2018 ; Frahm et al. 2022 ). This paper builds on previous research exploring the application of FTIR to lithic residue analysis, with a specific focus on the reliability of detection and classification of adhesive residues (Boeda et al. 1998 ; Bradtmoller et al. 2016; Kozowyk et al. 2020 ; Despotopoulou et al. 2024 ). While destructive techniques including Gas Chromatography-Mass Spectrometry (GC-MS) have been highly effective for identifying specific chemical compounds related to adhesives (Degano et al. 2019 ; Deviese et al. 2020 ; Chen et al. 2021 ; Chasan et al. 2024 ), their invasive nature makes them less desirable for early-stage screening or analysis of limited archaeological materials (Prinsloo et al. 2014 ; Degano et al. 2019 ). However, the reliability of FTIR data is highly dependent on the development of robust protocols, experimental validation, analyst skill, and clearly defined spectral libraries (Weiner 2010 ; Bruni and Guglielmi 2014 ; Lettieri 2015 ; Monnier et al. 2017a , 2017b ; Monnier 2018 ; Cortea et al. 2023 ). While reference libraries are essential for interpreting organic residues, they are inherently limited when applied to archaeological materials, which often exhibit complex and site-specific diagenetic transformations (Langejans 2010 ; Monnier 2018 ; McAdams et al. 2021 ). These transformations can alter the chemical structure and spectral signatures of organic residues in ways that are not easily predicted or replicated in controlled experimental conditions. As a result, it is unlikely that comprehensive reference libraries encompassing all possible post-depositional scenarios will ever be developed. The role of inter-instrument variability remains a consideration for spectral reproducibility as well. Recent studies by Pothier-Bouchard et al. (2019) and Quiles et al. ( 2022 ) have applied this concept, testing various instruments in both portable and benchtop formats, to assess collagen preservation in zooarchaeological and human bone samples. The results demonstrated that spectral data are not fully interchangeable between instruments. Discrepancies were attributed to differences in spectral resolution, signal-to-noise ratio, and analytical configuration (Pothier-Bouchard et al. 2019; Quiles et al. 2022 ). These findings indicate that different instruments introduce variability in spectra, with possible additional factors of influence including compositional variation in source material and unstandardized post-depositional transformation of samples. In cases related to adhesive studies, minor changes in recipe or adhesive processing steps may have a profound impact on the success of spectral reproducibility. Likewise, intra-instrument – and even intra-analyst – spectral variability is a viable concern, particularly for the application of reflectance mode spectral collection on samples with fluctuations in surface topography and matrix structure. Chemometrics, when applied to FTIR, has the goal of extracting chemical information from spectra with or without exact chemical identification. The approach typically utilizes multivariate statistical modeling to handle the large number of variables inherent to spectral datasets and aid in classification or quantification. Principal component analysis (PCA) offers a valuable multivariate tool for reducing the complexity of spectroscopic datasets and visualizing underlying patterns of variance that may correspond to compositional differences (De Benedetto et al. 2005 ; Baxter 2006 ; Chatterjee et al. 2018; Piña-Torres et al. 2018 ; Wertz et al. 2022 ). In the context of FTIR residue studies, where spectra can be influenced by a combination of material chemistry, substrate interference, and instrumental variability, PCA can aid in extracting dominant trends while minimizing the confounding effects of spectral noise and baseline variation (Medeghini et al. 2015; Piña-Torres et al. 2018 ; Wertz et al. 2022 ). By transforming spectral data into principal components that capture the most significant sources of variance, PCA provides a data-driven means of assessing whether adhesives with distinct chemical formulations naturally cluster in multivariate space (Piña-Torres et al. 2018 ). This can complement visual and library-based identifications by revealing relationships not immediately evident in spectral data, especially when dealing with complex or composite residues. To address known challenges, this study employs an experimental approach. First, we present the comparative results of FTIR analyses conducted on a suite of experimentally produced adhesives, chosen to replicate common substances utilized in prehistoric hafting practices. These samples were analyzed using two different FTIR microscopes across an eight-year interval, allowing us to assess instrument variability and reproducibility of spectral results. To a lesser extent, intra-instrument variability due to sample positioning was also tested. Next, by applying both analyst specific visual classification and statistical clustering techniques via principal component analysis (PCA) and hierarchical cluster analysis (HCA), we explore how compositional and contextual factors influence spectral quality and interpretability and the robusticity of applying statistical methods to group materials of unknown composition. In this workflow, we see grouping of samples as a necessary prerequisite to the eventual identification of individual adhesive components. Additionally, we briefly discuss the effects of substrate interference, especially when adhesives are transparent or present in thin layers or small deposits. A significant challenge in FTIR-based residue analysis lies in distinguishing between the spectra of the adhesive and those of the underlying lithic material (Coats 2000 ; Nunziante-Cesaro and Lemorini, 2012 ; Beasley et al. 2014 ; Glavcheva et al. 2014 ). This is especially true when the substrate, including chert or quartz, produces strong reflectance signals that may dominate or obscure the adhesive’s own signatures. During this analysis, transparent substances applied to flint substrates illustrate how factors including residue thickness and surface reflectivity impact the diagnostic quality of the spectra. This study emphasizes the importance of experimental approaches in validating and refining residue analysis methods in archaeology, highlighting key methodological variables that must be accounted for such as the influence of storage conditions, machine variability, substrate interference, diagenetic effects, and analyst subjectivity (Langejans 2010 ; Nucara et al. 2020 ; Dominici et al. 2022 ; Frahm et al. 2022 ). By generating new reference spectra of composite adhesives and evaluating instrument-to-instrument accuracy as well as the applicability of FTIR and PCA/HCA for adhesive residue classification, we aim to contribute to the development of a more standardized and reliable framework for residue analysis on lithic tools. The real strength of the present approach lies in providing a methodological framework for navigating the interpretive uncertainty associated with degraded or chemically altered archaeological materials (De Benedetto et al. 2005 ; Gallello et al. 2013). By applying multivariate techniques such as PCA and HCA, it becomes possible to identify spectral patterns based on compositional similarity of a defined collection, regardless of whether those residues match pristine modern references. This strategy enables analysts to detect meaningful groupings of adhesives within a single site or across related assemblages, even when the exact identity of the materials cannot be confidently determined. In doing so, it offers a scalable and reproducible tool for exploring technological variation in ancient adhesive use, despite the interpretive limitations imposed by diagenesis. Ultimately, the accurate identification or classification of residues, particularly adhesive compounds, has far-reaching implications for understanding prehistoric technologies (Mazza et al. 2006 ; Wadley 2010 ; Zipkin et al. 2014 ; Schmidt et al. 2024 ). Adhesive production often requires complex multi-step processes, involving informed resource selection, knowledge of material properties, skilled manipulation, and controlled application of heat or additive treatments (Regert et al. 2001 ; Wadley et al. 2009 ; Rageot et al. 2019 ; Chasan et al. 2021). These processes are indicative not only of technological sophistication but also of advanced cognitive capacities, including planning depth, working memory, and an understanding of cause-and-effect relationships (Wadley 2010 ; Wilkins et al. 2012; Wragg Sykes 2015 ). As such, every methodological advancement in classifying or identifying, and interpreting these residues deepens our ability to reconstruct the technological and cognitive landscapes of ancient human populations. 2. Methods 2.1 Materials Materials analyzed in this study consist of experimental adhesive samples deposited onto flint flakes prepared by an experienced primitive technologist (Christian Lepers, TraceoLab, Liège, Belgium). All samples were unaltered by post-depositional processes or experimental degradation, stored over the 8-year span in individual plastic bags in a temperature stable and light-safe location. Though not directly comparable to archaeological specimens, they serve as a useful basis for examining spectral behavior and assessing stability in peak presence within the individual substances. The methodological complications observed in this study would likely be amplified in archaeological contexts due to greater material degradation overall, degradation initiated by removal from the substrate, possible contamination, and other environmental factors. Samples consisted of base ingredients with different additives to represent various types of natural adhesive materials commonly available within prehistoric contexts, plus other more modern representations of natural glues. Base ingredient types included pine and spruce resins ( Pinus nigra and Picea abies ), acacia gum, birch tar, sinew glue, bone glue, and hide glue. Additives included beeswax (up to 50%), charcoal, flax, ochre, beef fat (10–20%), sand (10–20%), and clay (10–20%) (Table 1 ). All components were measured by weight in grams. Table 1 Composition of experimental adhesive samples with corresponding additive proportions and group assignments. Each sample is listed with its primary adhesive, as well as proportions of the primary adhesive, beeswax, and other additives (OA), along with the groups designated by analysts 1 and 2 during analyst-defined visual classifications used in the study. Adhesive recipes were either drawn directly from or inspired by the corresponding references. Sample ID Primary Ingredient Resin/Glue % Beeswax % Other Additives (OA) OA % Reference Group (1st) Group (2nd ) 91 − 01 / 91 − 35 Pinus nigra corsicana 100% - - - Degano et al. 2019 3 3 91 − 02 / 91 − 36 Pinus nigra corsicana 50% 50% - - Degano et al. 2019 ; Sano et al. 2019 3 5 91 − 03 Picea abies 100% - - - Helwig et al. 2014 ; Degano et al. 2019 5 5 91 − 04 Picea abies 50% 50% - - Helwig et al. 2014 ; Degano et al. 2019 ; Sano et al. 2019 5 5 91 − 05 Acacia gum 100% - - - Wadley 2005 6 6 91 − 06 Acacia gum 50% 50% - - Wadley 2005 ; Sano et al. 2019 5 5 91 − 07 Birch tar 100% - - - Regert et al. 1998 ; Mazza et al. 2006 ; Niekus et al. 2019 2 2 91 − 08 Beeswax 0% 100% - - Baales et al. 2017 5 5 91 − 09 Pinus nigra corsicana 80% - Charcoal 20% Rots et al. 2011 ; Bradtmoller et al. 2016; Degano et al. 2019 3 3 91 − 10 Pinus nigra corsicana 40% 40% Charcoal 20% Rots et al. 2011 ; Bradtmoller et al. 2016; Degano et al. 2019 1 5 91 − 11 Pinus nigra corsicana 80% - Flax 20% Wadley 2005 ; Shaham et al. 2010 ; Degano et al. 2019 3 3 91 − 12 Pinus nigra corsicana 40% 40% Flax 20% Wadley 2005 ; Shaham et al. 2010 ; Degano et al. 2019 2 3 91 − 13 Pinus nigra corsicana 80% - Ochre 20% Rots et al. 2011 ; Bradtmoller et al. 2016; Degano et al. 2019 3 3 91 − 14 Pinus nigra corsicana 40% 40% Ochre 20% Rots et al. 2011 ; Bradtmoller et al. 2016; Degano et al. 2019 5 5 91 − 15 Sinew glue (commercial) 100% - - - Cnuts et al. 2017 ; Tydgadt and Rots 2022 4 4 91 − 16 Sinew glue (experimental) 100% - - - Cnuts et al. 2017 ; Tydgadt and Rots 2022 4 4 91 − 17 / 91 − 33 Bone glue (commercial) 100% - - - Tydgadt and Rots 2022 4 4 91 − 19 / 91 − 34 Deer hide glue (experimental) 100% - - - Tydgadt and Rots 2022 4 4 91 − 20 Sheep hide glue (experimental) 100% - - - Tydgadt and Rots 2022 4 4 91 − 21 Picea abies 70% 30% - Helwig et al. 2014 ; Degano et al. 2019 5 5 91 − 22 Picea abies 90% - Beef fat 10% Regert et al. 1998 ; Helwig et al. 2014 ; Degano et al. 2019 2 2 91 − 23 Pinus nigra corsicana 90% - Beef fat 10% Regert et al. 1998 ; Degano et al. 2019 3 3 91 − 24 Picea abies 90% - Beef fat - overheated 10% Regert et al. 1998 ; Helwig et al. 2014 ; Cnuts et al. 2017 ; Degano et al. 2019 2 2 91 − 25 Picea abies 90% - Beef fat – frequently heated 10% Regert et al. 1998 ; Helwig et al. 2014 ; Cnuts et al. 2017 ; Degano et al. 2019 5 5 91 − 26 Picea abies 80% - Ochre 20% Rots et al. 2011 ; Helwig et al. 2014 ; Bradtmoller et al. 2016; Degano et al. 2019 1 1 91 − 27 Picea abies 80% - Ochre + Sand 10% + 10% Bradtmoller et al. 2016; Degano et al. 2019 1 1 91 − 28 Picea abies 80% - Ochre + Clay 10% + 10% Helwig et al. 2014 ; Degano et al. 2019 1 1 91 − 29 Picea abies 80% - Sand 20% Helwig et al. 2014 ; Degano et al. 2019 1 1 91 − 30 Picea abies 80% - Clay 20% Helwig et al. 2014 ; Degano et al. 2019 1 1 91 − 31 Picea abies 80% - Beef fat + Sand 10% + 10% Regert et al. 1998 ; Helwig et al. 2014 ; Degano et al. 2019 2 2 91 − 32 Picea abies 80% - Beef fat + Clay 10% + 10% Regert et al. 1998 ; Helwig et al. 2014 ; Degano et al. 2019 2 2 2.2 Methods The initial round of FTIR analysis took place in 2017–2018 at the University of Tübingen, where approximately 140 spectra were generated with additional scans collected as needed. These spectra were obtained using an Agilent 610 FTIR microscope equipped with a potassium bromide (KBr) beamsplitter and a wide band, liquid nitrogen cooled mercury-cadmium-telluride (LN-MCT-B) detector operated in reflectance mode. The spectral range of FTIR instruments is determined by a number of different factors, including the source, beamsplitters and detectors; this particular combination yields a spectral range of 4000 − 400 cm⁻¹ in reflectance mode, of which 4000 − 450 cm⁻¹ is generally usable. The microscope was coupled to an Agilent 660 bench FTIR instrument with ceramic source, running the Resolutions Pro software. Background scans were collected on gold, and the spectral resolution was set at 2 cm⁻¹. The exact number of co-added scans varied between 180–200 per spectrum. For each sample, spectra were typically collected from the adhesive deposit using the largest analytical area available (approximately 600 x 600 µm) in order to reduce spectral noise. In cases where inclusions were present, spectra were also collected from overlapping areas with reduced aperture; however, due to poor spectral quality, these are largely excluded from the present study. Examples of sampling conditions are illustrated in Fig. 1 . The second round of data collection took place in 2024–2025 at the TraceoLab (University of Liège) and was carried out using a Bruker LUMOS II FTIR microscope, also in reflectance mode. This instrument was equipped with an internal ceramic source, a zinc selenide (ZnSe) beamsplitter and a thermoelectrically cooled mercury-cadmium-telluride (TE-MCT) detector with a spectral range of 4000 − 650 cm⁻¹, operating through the Bruker OPUS software. As in the earlier analysis, background scans were collected on gold, and spectral resolution was maintained at 2 cm⁻¹. Scan parameters were standardized at 400 co-added scans per spectrum, with a uniform aperture of 400 × 400 µm for all measurements. The instrumentation and sampling parameters are summarized in Table 2 and the two systems are hereafter referred to as KBr/LN-MCT-B and ZnSe/TE-MCT-A. It should be noted that these two systems represent two very common configurations of optical components for laboratory-grade instruments, with several different manufacturers producing similar bench-coupled and stand-alone FTIR microscopes. TE-MCT detectors are typically available on newer models. Wide-band versions (designated as MCT-B) have broader spectral range, but lower sensitivity compared to MCT-A (sometimes called “high-sensitivity”). Samples were minimally handled and mounted on glass slides or molded platforms covered with parafilm during analysis. For both the initial and second round of spectra collection, each sample underwent scans at a minimum of three points, and in some cases up to eight, to ensure representational spectral quality across the sample area and to account for the potential variability caused by inclusions. Due to the level of topography within the analytical areas, attention was also paid to the field of view focus, maximizing the area that would yield a good signal. Despite automated mapping being available on both systems and in similar systems available from other manufacturers, all analyses were conducted manually due to the need to significantly adjust the focus between points. During the 2024/2025 analysis, four of the original samples could not be reanalyzed and thus were reproduced to the same specifications. One piece that was originally included, 91 − 18, has been excluded from this study because it could not be replicated for the second component of analysis. Table 2 FTIR instrument configurations used in this study. Instrument IR Source Beamsplitter Detector Spectral range Resolution applied Aperture applied Agilent Cary 660/610 Ceramic (external via the 660) Potassium-bromide (KBr) Liquid nitrogen cooled Mercury-cadmium-telluride type B (LN-MCT-B) 4000-400cm⁻¹ 2cm⁻¹ 600 x 600 µm Bruker LUMOS II Ceramic (internal) Zinc-selenide (ZnSe) Thermoelectrically cooled Mercury-cadmium-telluride type A (TE-MCT) 4000-650cm⁻¹ 2cm⁻¹ 400 x 400 µm Prior to visual assessment by the analysts, all spectra were processed by removing carbon dioxide peaks to reduce atmospheric interference, and a Kramers-Kronig transformation (KKT) was applied to convert reflectance spectra into a form that more closely approximates transmission spectra. The application of KKT fundamentally alters the shape of reflectance spectra by correcting for dispersive distortions inherent in reflectance measurements, with such distortions often producing features reminiscent of spectral derivatives (e.g. a signal below the baseline just before a strong peak), strong inverted bands (reststrahlen bands), baseline shifts, and adjustments in peak intensity and position that may alter interpretation (Vetter and Schreiner 2011 ; Miliani et al. 2012 ; Monico et al. 2013 ; Prinsloo et al. 2014 ; Invernizzi et al. 2018 ). Spectra were then categorized into high, medium, or low quality based on the presence of visible peaks and their signal-to-noise ratios. These steps were carried out using Resolutions Pro and OPUS software, respectively. Any sample that yielded only one high-quality spectrum was re-analyzed to obtain additional data for more robust classification. To assess the spectral similarity between instruments and evaluate reproducibility, RStudio version 4.4.3 equipped with tidyverse (Wickham et al. 2019 ), readxl (Wickham and Bryan 2025 ), pracma (Borchers 2023 ), stringr (Wickham 2023 ), ggplot2 (Wickham 2016 ), FactoMineR (Le et al. 2008 ), factoextra (Kassambra and Mundt 2020 ), and missMDA (Josse and Husson 2016 ) packages was used to apply several statistical methods to paired spectra across three conditions: (1) raw data, (2) KKT-transformed data without intensity normalization, and (3) KKT-transformed data with standard normalization of a maximum %Reflectance y-value of 0.5 applied. These applied statistics included average difference and standard deviation, root mean square error (RMSE), coefficient variance (CV), cosine similarity, Pearson’s correlation coefficient (PCC) Wilcoxon signed-rank test (p), and Cohen’s d. Due to differences in spectral range between the two machines, spectra from the KBr/LN-MCT-B were truncated to 4000 − 650 cm⁻¹ to align with the range of the ZnSe/TE-MCT-A-generated spectra for both comparative testing. To preserve as many original wavenumbers as possible, no smoothing was performed. While this precise combination of statistical methods (RMSE, CV, cosine similarity, Wilcoxon test, and Cohen’s d) in the context of paired FTIR spectra has not, to our knowledge, been directly described in existing spectroscopy literature, each metric is firmly rooted in established analytical and chemometric practice. Cosine similarity is widely used to quantify spectral shape agreement in areas such as near-infrared analysis and mass-spectrometry (Bittremieux et al. 2022 ; Guo et al. 2025 ). RMSE and coefficient of variation are standard figures of merit for evaluating quantitative model performance in chemometrics (Sila et al. 2016 ; Ghosh et al. 2021 ; Guo et al. 2025 ). Furthermore, reproducibility and stability assessments in mass and Raman spectroscopy frequently utilize correlation-based measures, intensity variation, clustering, and spectral similarity metrics (Guo et al. 2025 ). Therefore, our comprehensive application of these complementary metrics provides a rigorously grounded, multidimensional framework for evaluating spectral similarity and reproducibility between instruments, anchored in the broader methodological landscape of analytical spectroscopy and chemometrics. Intra-instrument variability and the effects of sample orientation on spectral reproducibility were also tested with a targeted subset of adhesive samples with known additives and visible inclusions. For each sample, multiple spectra were collected from the same point of analysis but with the sample reoriented on the FTIR platform. For each subsequent spectra, the sample was rotated by 90° in a counterclockwise direction. All scans were collected using the ZnSe/TE-MCT-A system, following the same parameters as described previously. This was done to simulate the inherent variability in archaeological analyses, where there is no standardized convention for how a lithic specimen should be oriented on the microscopic stage. In practice, orientation is often determined by the analyst’s judgement, guided by factors such as artifact morphology, surface accessibility, or stability on the stage; thus, artifact staging for spectral acquisition can vary widely across studies. Our tests therefore evaluated whether such orientation differences affect spectral shape, alter peak intensities, or introduce distortions linked to polarization effects or reflectance behavior associated with surface topography and anisotropy. The variability between orientation-shifted spectra was assessed with RStudio version 4.4.3 equipped with dplyr (Wickham et al. 2025 a), ggplot2 (Wickham 2016 ), tidyr (Wickham et al. 2025 b), stringr (Wickham 2023 ), effsize (Torchiano 2019 ), and broom (Robinson et al. 2025 ) packages. Statistical methods applied included calculations for standard deviation, coefficient of variation, Pearson correlation coefficient, cosine similarity, Cohen’s d, Wilcoxon signed-rank test (p), and Bland-Altman bias and limits of agreement. Within R, data was reshaped into wide format to enable pairwise comparison within each sample group, yielding six orientation pairs per sample, with calculations run for each pair. For the steps of classification and comparison, KKT-processed spectra without normalization and with CO2 peaks removed were evaluated using a reverse library search method within the Essential FTIR and OPUS software packages. All instrument specific spectra were compiled into a single reference library per machine and compared individually to assess similarity. Spectra from the KBr/LN-MCT-B were classified by a single analyst in Tübingen, and spectra from the ZnSe/TE-MCT-A instrument were classified by a separate analyst in Liège. Visual inspection by analysts was prioritized over automated rankings and library hit matches, as human evaluation often produced more accurate assessments of spectral similarity. To reflect a workflow that may be applied in archaeological settings, initial visual grouping of spectra was undertaken prior to identification efforts. This approach mirrors real-world scenarios in which ancient residues, having undergone diagenetic effects, likely will not match modern reference materials directly. In such cases, classification based on internal consistency across the assemblage can reveal shared chemical profiles, helping to identify sets of artifacts with similar residues even when external reference matches are unavailable. Thus, the analysts began by grouping spectra based on shared peak positions and intensities to establish internal structure within the datasets before pursuing material-specific identification. Initial classification by analyst 1 grouped the spectra into nine categories, which were later refined into six distinct groups based on shared spectral characteristics. Initial classification by Analyst 2 grouped the spectra into eight categories, which were later refined into six with the knowledge that Analyst 1 had also arrived at six final groups, thereby facilitating direct comparison. Only peak positions and intensities were used for classification. At the time of analysis and initial grouping assignments, analysts were blind to sample composition as well as the results of the other analyst’s groupings, ensuring unbiased classification. The final consolidation into six aligned groups incorporated reference to analyst 1’s classification scheme, allowing the results presented here to be directly comparable. While external spectral libraries were consulted for later material identification, they played only a secondary role in identification due to interpretive challenges, with primary identification relying on visual comparison, direct peak matching, and spectral overlay. When performing a side-by-side comparison of spectra from each machine, the highest quality spectrum for each sample was manually selected based on signal-to-noise ratio and peak intensity. In the final stage of the visual classification, analyst 2 compared the six groups identified by each analysis and renumbered them, applying the same number when groups broadly matched. These groups are summarized in Table X under Group (1st ) and Group (2nd ). All images created for visual comparison between groups were produced in the Peak Spectroscopy software (Operant LLC). Post-classification, external libraries were utilized to interpret the composition of materials to known standards. Libraries utilized include both reflectance spectra from the University of Minnesota Archaeological Materials Infrared Spectra Library and the Infrared and Raman Users Group (IRUG), and transmission spectra from the Helen and Martin Kimmel Center for Archaeological Science Infrared Spectra Library, IRUG, and the InfraArt Spectra Library (Price and Pretzel 2007; Monnier et al. 2017a , 2017b ; Cortea et al. 2023 ; Weizmann Institute of Science). Comparison to the external reference libraries of spectra in transmission mode was performed visually to assess the reliability of KK-transformed reflectance spectra to align with those generated in transmission mode. All images created for visual comparison of samples with reference spectra were produced with Peak Spectroscopy. PCA was applied to spectral data to explore patterns of variance within the collection in addition to analyst performed classification. All multivariate statistical analyses were conducted using PLS Toolbox version 9.5 (Eigenvector Research Inc.), which runs in a MATLAB R2024b (Mathworks) environment. To compare the datasets for chemometric modeling, a consistent preprocessing strategy was applied across both ZnSe/TE-MCT-A and KBr/LN-MCT-B data. Prior to preprocessing, spectra were truncated to the region between 1800–650 cm⁻¹ to isolate the fingerprint region and then smoothed with a 17-point Savitzky–Golay (S-G) filter in Peak Spectroscopy to reduce noise while preserving peak shape. To assess the influence of the spectral range beyond the capabilities of the ZnSe/TE-MCT-A instrument on pattern detection, additional PCA was performed on KBr/LN-MCT-B datasets to the extended fingerprint zone from 1800 − 400 cm⁻¹, also treated with 17-point S-G smoothing. Extremely noisy or low-quality spectra as well as spectra from inclusions within the adhesive were excluded from the PCA to assess for true variance of the adhesive matrix. Multiple preprocessing strategies were tested before defining the most productive method. For the final PCA model reported here on KBr/LN-MCT-B datasets, to reduce multiplicative scatter and path-length variability, standard normal variate (SNV) preprocessing was applied as a normalization method, ensuring that the model would highlight relative variance rather than absolute signal intensity. In a similar application to Wertz et al. ( 2022 ), generalized least squares weighting (GLSW) with a threshold of 0.05 was then applied to declutter the data, followed by mean centering. For ZnSe/TE-MCT-A models, the aforementioned preprocessing steps were similarly applied, however application of a 1st derivative (2nd polynomial) prior to SNV proved beneficial, and the application of mean centering had no influence on the results so this step was excluded (Fig. 2 ). Model robustness was assessed with Venetian-blind cross-validation with 10 folds and 3 samples per blind, with models run both with and without the cross-validation. These models were applied individually on raw and KKT treated data across both ZnSe/TE-MCT-A and KBr/LN-MCT-B systems. PCA was conducted also on two different grouping strategies, one based on analyst-defined classes developed through visual interpretation of spectral patterns without prior knowledge of adhesive composition, and another based on adhesive-defined classes reflecting the known chemical formulations of each sample defined by the predominant adhesive component (pinus-based, spruce-based, gum-based, protein-based, birch tar, and pure beeswax). The groups defined by predominant adhesive component are listed in Table 1 , under the heading “Primary ingredient”. Beeswax was always considered a secondary component, even when present in equal amounts as the primary. Following successful PCA, unsupervised cluster analysis was performed HCA in PLS Toolbox to evaluate whether natural groupings of adhesive types could be recovered from the data. HCA was conducted both directly on the preprocessed spectral data and on the PCA-transformed scores to compare the effectiveness of each approach. Running HCA on the spectral data alone preserved the full dimensionality and fine-grained spectral information, allowing an assessment of how well adhesives could be clustered without dimensionality reduction. However, HCA was also performed on the PCA scores, using the first three principal components, to reduce noise and emphasize the most informative variance while minimizing the influence of minor spectral fluctuations and measurement artifacts. The selection of three PCs was based on their cumulative explanation of the majority of structured variance within the dataset, ensuring a balance between retaining meaningful chemical distinctions and avoiding overfitting to noise-dominated components. HCA was performed using Ward’s method as the linkage criterion, which optimizes cluster formation by minimizing within-cluster variance, favoring compact and distinct groupings which present an advantage when dealing with overlapping or compositionally complex adhesive classes. Clustering was applied to the full datasets, as well as to reduced subsets that included either one representative spectrum per sample or a dataset with every other sample excluded, to assess the impact of replicate redundancy on clustering performance. This dual approach of testing HCA with and without PCA enabled a comprehensive evaluation of clustering robustness and clarified the benefits of dimensionality reduction in improving class resolution in spectroscopic datasets (Capobianco et al. 2017 ). The selection of PCA and HCA in this study is grounded in their demonstrated utility for simplifying high-dimensional spectroscopic data and detecting chemically meaningful patterns. These methods are especially effective for datasets where overlapping signals, baseline shifts, and instrument noise complicate direct interpretation. While relatively underutilized in archaeological applications, PCA and HCA have proven successful in related domains, such as forensic residue analysis (Materazzi et al. 2017 ; Sharma and Sharma 2022 ), biomedical diagnostics (Wang and Mizaikoff 2008 ; Rohman et al. 2019 ), and food science (Bendini et al. 2007 ; Rahmania et al. 2015; Indrayanto and Rohman 2020 ; Hadaruga et al. 2022 ), where compound identification and material classification face similar spectral complexity. The use of such methods here reflects growing interest in applying chemometrics to archaeological FTIR data to enhance classification accuracy and reduce the subjectivity of visual interpretation. By pairing PCA with HCA, we increase model strength and interpretive confidence, particularly in assessing the internal consistency of adhesive groupings and evaluating the impact of preprocessing choices. This approach offers a methodological scaffold for future archaeological residue studies seeking to integrate multivariate tools, especially when diagenesis or the absence of direct reference materials limits traditional classification strategies. To test the robusticity and interpretive limits of the applied chemometric models, a blind validation set was included. This set consisted of ten samples, each composed of pure (100%) substance applied to a flint substrate, representing at least four of the six subsets (pinus-based, spruce-based, gum-based, birch tar, protein-based, and pure beeswax) from the primary adhesives within the main dataset. Unlike the composite mixtures used for model calibration, these validation samples were chemically simple, serving to evaluate how well the PCA model trained on complex, multi-component adhesives could handle single-component inputs of similar origin. The exact composition of each sample remained unknown to the analyst during scanning and analysis. Each sample was scanned two times under identical parameters using the ZnSe/TE-MCT-A system, generating 20 spectra in total. Following identical spectral processing parameters, the validation spectra were then projected onto the PCA models derived from the training datasets established with ZnSe/TE-MCT-A data. After projection, the distribution of validation points was evaluated relative to the PCA-defined clusters of the known training samples. Predicted class membership was inferred based on proximity in multivariate space. Once this was completed, the true adhesive composition of the validation samples was revealed and compared to the PCA-based predictions. This validation procedure was not designed exclusively as a conventional performance metric, but rather as a probe into the model’s behavior when confronted with datasets of fundamentally different structures as an attempt to test the scope and limitations of PCA as an exploratory tool for adhesive residue analysis. Because the PCA models were built on complex mixtures containing overlapping components, their ability to isolate and interpret pure inputs was expected to be somewhat limited. As such, the exercise served to highlight interpretive risks and provide insight into where and why the model may fail, information that is critical for understanding the broader applicability and limitations of PCA-based residue classification of adhesives. 3. Results 3.a. Instrument comparison and spectral processing Comparison of raw versus processed FTIR spectra revealed substantial improvements in cross-instrument alignment following application of the KKT, both with and without normalization. The mean Pearson correlation increased from − 0.256 in raw spectra to + 0.352 in both datasets post-processing, indicating a shift from inverse or negligible linear relationships to moderate positive agreement. Concurrently, the RMSE between spectra dropped sharply from 1.37 to 0.14 in KKT-not-normalized data and 0.18 in KKT-normalized data, underscoring the ability of the applied processing steps to reduce amplitude-related discrepancies. Effect size, measured by Cohen’s d, decreased from an extreme value of -5.69 in raw spectra to -0.78 in KKT-not-normalized and − 0.69 in KKT-normalized data, suggesting that instrument-based differences diminished considerably post-processing. Similarly, the standard deviation of differences shrank from 0.44 in raw to 0.09 in KKT-not-normalized and 0.13 in KKT-normalized data, reflecting greater consistency across spectra after processing. Interestingly, cosine similarity, which addresses vector shape independent of magnitude, dropped from 0.95 in raw spectra to 0.27 in both processed versions. This suggests that while KKT significantly improved alignment in amplitude and trend, it also introduced distortions in spectral shape, which presents a tradeoff that may affect analyses reliant on peak ratios or shape-based classification. Such processing may diminish performance in analyses reliant on relative peak structure, ratios, or chemometric classification, despite enhancing numerical comparability across instruments. Spectra generated with the KBr/LN-MCT-B were in general fairly comparable to those generated with the ZnSe/TE-MCT-A, though there were often differences in peak height and occasional shifts in peak position, varying by up to 25 wavenumbers at most but on average falling within the expected range of variation between 2–10 cm⁻¹ (Hofko et al. 2018 ; Nemeth et al. 2025 ; Nicolau and Matzger 2024 ; United States Pharmacopeia 2012). Peaks were usually skewed toward lower positions but in some cases also shifted higher. Most occurrences of shifted wavenumbers tend to occur within the OH and CO functional groups (Table 2 ). Aside from these differences, variation between the machines is primarily presented in spectral baseline, peak amplitude, and signal-to-noise ratio. Difference in signal-to-noise ratio is attributed primarily due to the increased number of co-added scans incorporated in the ZnSe/TE-MCT-A spectra. Despite these variances, spectral patterning was consistent between machines, aside from a few cases where the differences may be linked to inclusions present within the residue itself as opposed to instrument variability. Some instances of dissimilarity are also linked to the four samples which were reproduced for the second round of analysis, which could speak to the uncontrolled variation possible within production despite the employment of identical techniques and formulas. With the same spectral processing steps taken, the transformed data are less variable between the two machines than the raw data, with differences being generally minimal and not influencing material interpretation (Fig. 3 ). One of the more evident differences present is that the low end of the ZnSe/TE-MCT-A spectral range was limited to 650 cm⁻¹, preventing the collection of data points to the same range as the KBr/LN-MCT-B as well as substantially increasing the noise present in the lower range of spectral limits within the ZnSe/TE-MCT-A-generated spectra. While this limitation was not drastic for the samples analyzed here, it could present a challenge when interpreting materials of other origins, particularly inorganic compounds such as mineral-based residues. Despite the minor differences observed between the spectra from each machine, visual group classification did not vary significantly and results were consistent with only 3 outliers (in the second classification attempt, 91 − 10 was assigned to Group 5 as opposed to Group 1; 91 − 12 was assigned to Group 3 as opposed to Group 2; and 91 − 02/36 was assigned to Group 5 as opposed to Group 3). The results of the intra-instrument variability, tested on the ZnSe/TE-MCT-A system, showed that orientation changes induced spectral changes in some but not all samples tested. There is a clear differentiation between those that appear quite stable despite shifting the orientation of the sample (91 − 26 and 91 − 29), and those which appear sensitive to the changes (91 − 11). There was also some intermediate behavior observed among tested samples which contained clay as an admixture (91 − 28, 91 − 30, 91 − 32). This likely originates from the area selected for analysis, with the area analyzed for 91 − 11 containing a distinct vegetal fiber embedded within the resin mixture whereas 91 − 26 and 91 − 29 displayed more consistency in the residue surface despite being admixtures. 91 − 26 and 91 − 29 demonstrated high reproducibility across all orientations, exhibiting minimal pointwise difference with consistently low SDs and near-perfect correlation between the respective spectra. Cohen’s d values hovered around zero, further supporting that orientation had minimal effect on resulting spectra. In contrast, the spectra from 91 − 11 exhibited strong orientation-dependent variation, both in magnitude and shape. Spectra from 91 − 28, 91 − 30, and 91 − 32 were generally stable across orientations, though some pairings revealed modest variation in intensity or shape. These minor deviations may be attributed to subtle surface texture differences or interactions between the organic matrix and mineral inclusions. Such possible variability within a single area introduces substantial challenges for exact reproducibility, especially within archaeological contexts, and serves as a point of caution for generating diagnostic interpretations from limited test areas. These findings, supported by PCA results discussed in section 3 .c., underscore a key methodological gap: unlike other lithic analysis techniques such as illustration, no standard convention exists for orienting lithics in reflectance FTIR. We suggest that orientation should not be overlooked, and, at minimum, analysts should document sample positioning during measurement and for heterogenous residues, it may be advisable to collect spectra from multiple orientations. Alternatively, developing standard orientation protocols (e.g. aligning visible flow fabrics along a fixed axis) could help mitigate variability and improve reproducibility in archaeological applications. 3.b. Classification Overview and Group-by-Group descriptions The classification of the FTIR spectra into six distinct groups reflects meaningful compositional differences among the samples while also taking into consideration the role of visual identification skills from the analyst. Groupings appear to be primarily driven by the presence or absence of specific organic materials, with groups independently dominated by tree resins, beeswax, animal fat, ochre, or animal glues, and the patterning of their associated infrared absorption peaks. The diversity of functional groups provides a chemical basis for some of the observed classes, though the majority of variation lies in the fingerprint region. Amidst the spectral differences that led to the unique classification groupings (Table 3 ), there is some noticeable overlap in peak presence which indicates an incidence of similarity in material composition across the samples. This can be useful in addressing composite materials that display a conglomeration of peaks representative of different compositional elements. This study also helps to highlight the differences that can be present within such conglomerate materials, lending potential references for attempting to identify unknown adhesives based on presence or absence of known peaks. Noted differences in peak position between the two machines, though minor, may also indicate shifts that occur over time due to degradation of materials. Table 3 Principal wavenumber assignments and corresponding molecular interpretations for the six analyst-defined visual group classifications which were congruent between the two analysts working with spectra from both the KBr/LN-MCT-B and ZnSe/TE-MCT-A instruments. Differences in peak positions in wavenumber units between instruments are indicated in bold. This table includes the most prominent or diagnostically relevant peaks per group, with functional assignments and interpretive notes. Group Wavenumber (cm⁻¹) – KBr/LN-MCT-B Wavenumber (cm⁻¹) – ZnSe/TE-MCT-A Assignment Notes 1 3400 3380 O-H and N-H stretch Broad, common in phenols 1 2980 2945 C-H stretch Aliphatic chains, possibly methyl groups 1 2920 2920 C-H stretch Aromatics or aliphatic 1 2890 2870 C-H stretch Aromatics or aliphatic 1 2855 2855 C-H stretch Aliphatic CH₂ symmetric stretch 1 1695 1695 C = O stretch Carboxylic acids, esters 1 1606 1604 C = C stretch Aromatic double bonds 1 1516 1517 C = C stretch Characteristic of phenolic resins 1 1464 1464 CH₂ bend Aliphatic hydrocarbon 1 1456 1453 CH₂ bend Aliphatic chain deformation 1 1435 1435 CH₂ bend Aliphatic hydrocarbon 1 1386 1383 CH3 bend Aliphatic methyl deformation 1 1273 1276 C-O stretch Carboxylic acids 1 1238 1238 C-O stretch Esters or polysaccharides 1 1208 1211 C-O stretch Possible glycosidic or resin-derived 1 1169 1169 C-O-C or C-C stretch Terpenoids, esters 1 1126 1124 C-O-C or C-C stretch Terpenoids, resin acids 1 1035 1035 Si-O stretch Silicates 1 833 833 N-O stretch Possible nitrate contamination or silicates 1 776 776 Si-O bend Silicates 2 2957 2957 C-H stretch Aliphatic 2 2932 2932 C-H stretch Aliphatic 2 2857 2857 C-H stretch Aliphatic 2 1733 1731 C = O stretch Esters, ketones, carboxylic acids 2 1696 1695 C = O stretch Carboxylic acids, esters 2 1607 1607 C = C stretch Aromatic ring 2 1471 1473 CH₂ bend Aliphatic hydrocarbon 2 1456 1456 CH₂ bend Aliphatic chain deformation 2 1386 1386 CH3 bend Aliphatic methyl 2 1276 1276 C-O stretch Carboxylic acid or esters 2 1238 1238 C-O stretch Esters or polysaccharides 2 1179 1179 C-O stretch Possible phenol or terpene-related 2 1035 1035 Si-O stretch Silicates 3 3400 3400 O-H stretch or N-H stretch Broad, often in gums or proteins 3 2967 2965 C-H stretch Aliphatic methyl 3 2940 2938 C-H stretch Aliphatic CH₂ 3 2856 2856 C-H stretch Aliphatic CH₂ symmetric stretch 3 1715 1715 C = O stretch Carboxylic acids 3 1695 1695 C = O stretch Carboxylic acids or aged resins 3 1607 1603 C = C stretch Aromatic ring 3 1516 1516 C = C stretch Phenolic or aromatic 3 1464 1464 C-H bend Aliphatic chain 3 1455 1453 C-H bend Aliphatic 3 1435 1431 CH₂ bend Aliphatic hydrocarbon 3 1274 1276 C-O stretch Carboxylic acids or esters 3 1237 1237 C-O stretch Carbohydrates or esters 3 1210 1210 C-O stretch Gum or resin linkages 3 1124 1124 C-O-C or C-C Possible glycosidic or resin-derived 3 1034 1034 Si-O, C-O or C-C Silicates, or possible terpenoids or polysaccharides 3 856 856 Aromatic out-of-plane Possible aromatic bending 3 819 819 Aromatic or skeletal Out-of-plane bend 4 3350 3350 O-H stretch, N-H stretch Collagen, proteinaceous material 4 3088 3075 C-H, C = O, or N-H stretch Possible Amide overtones 4 1660 1660 C = O stretch, N-H stretch Amide I 4 1565 1565 C = O stretch, N-H stretch Amide II 4 1558 1558 C = O stretch, N-H stretch Amide II 4 1456 1455 CH₂, N-H bend Protein backbone 4 1283 1283 P = O Phosphorylated proteins 4 1242 1242 P = O Collagen 4 1205 1200 C-O stretch Amide III 4 1159 1154 C-N or C-O stretch Amide III or carbohydrates 4 1080 1080 Si-O stretch Silicates 4 1033 1027 Si-O or C-O stretch Silicates or polysaccharides 4 973 973 C-H deformation Lipids or hydrocarbons 4 718 718 CH₂ rocking Aliphatic chains 5 2921 2921 C-H stretch Aliphatic hydrocarbon 5 2852 2852 C-H stretch Aliphatic hydrocarbon 5 1739 1737 C = O stretch Esters 5 1696 1696 C = O stretch Degraded fats or mixed esters 5 1652 1652 C = C or Amide I Possible unsaturated fats or proteins 5 1558 1561 5 1473 1473 CH₂ scissor Lipids 5 1464 1464 CH₂ scissor Lipids 5 1386 1385 CH3 bend Lipids 5 - 1247 C-O Esters 6 3400 3375 O-H stretch Polysaccharides, gums 6 2950 2930 C-H stretch Polysaccharide ring systems 6 2900 2880 C-H stretch Sugars 6 1610 1610 COO- stretch Carboxylate salt, gums 6 1505 1505 C = C or NH bend Weak aromatic or proteins 6 1240 1250 C-O stretch Polysaccharides or ester 6 1080 1080 Si-O stretch Silicates 6 1040 1040 C-O-C stretch Polysaccharides, gums 6 980 980 C-H deformation Degraded organics Some specimens yielded multiple spectral classifications from different test points, attributed to the presence of inclusions from additives that strongly swayed the spectral results. Of the 31 samples included, all 31 were assigned to defined classification groups (1–6), with 11 showing additional absorption peaks beyond those used to define their grouping. Materials that are thin or translucent present particular difficulties. In this case, they often produced spectra which showed overlapping features representative of both the residue and the lithic substrate, resulting in noisier and potentially misleading outputs. Transparent adhesives were exclusive to Groups 4 and 6, implying that substrate interference can be particularly strong in these cases. This highlights the risk of misinterpreting translucent, small, or thin residues and emphasizes the need for extensive testing of background substrates to correctly identify adhesive materials. Group 1 (Fig. 4 ) consists primarily of spruce-based mixtures, often modified with ochre, clay, or sand. This group is characterized by a dense distribution of peaks across the mid-IR spectrum, with prominent peaks present in the functional group zone at about 3600 − 3250, 2950 − 2850, 1690, and 1600 cm⁻¹. Other prominent peaks are found within the fingerprint region, located at about 1515, 1465, 1425, 1370, 1275, 1235, 1210, 1170 − 1125, and 1035 cm⁻¹. The peaks observed in this group tend to correspond to aliphatic C–H stretching and bending, C = O stretching from esters, ketones, or carboxylic acids, aromatic C = C stretches, acidic C-O stretching, and various inorganic additives (Edwards et al. 1996 ; Edwards and Falk 1997 ; Socrates 2001 ; Vahur et al. 2011 ; Bruni and Guglielmi, 2014 ; Helwig et al. 2014 ; Duce et al. 2015 ; Monnier 2017a; Martin-Ramos et al. 2018 ). The wide array of peaks in this group supports complex, multi-component formulations with both organic and mineral components. Comparisons to reference spectra yielded some correlations to colophony from the INFRA-ART library, modern Pistacia resin from the Kimmel library, and spruce resin on English flint from University of Minnesota Archaeological Materials Infrared Spectra library. Group 2 (Fig. 5 ) includes samples composed mainly of spruce resins combined with beef fat and/or clay, as well as pure birch tar. These samples exhibit peaks similar to Group 1 in the 1700–1450 cm⁻¹ region, but with notable differences in peak shape and intensity—particularly at about 2960, 2930, 1735, and 1700 cm⁻¹ in the functional group region, and 1470, 1450, 1385, 1250, and 1175 cm⁻¹ within the fingerprint region. The peaks observed in this group tend to correspond with aliphatic C-H stretching and bending (Edwards 1996; Edwards and Falk 1997 ; Socrates 2001 ; Beltran et al. 2016 ; Martin-Ramos et al. 2018 ), C = O stretching from esters, ketones, or carboxylic acids (Edwards 1996; Cinta-Pinzaru et al. 2012 ; Monnier et al. 2017a ), acidic C-O stretching (Socrates 2001 ; Cinta-Pinzaru et al. 2012 ; Monnier et al. 2017a ; Schmidt et al. 2023 ; Schmidt and Koch 2024 ), and a minor phosphate peak at 1035 cm⁻¹. The presence of beef fat and flax may account for the unique combination of vibrations, and the relatively reduced number of total peaks suggests less chemically diverse compositions compared to Group 1 (Edwards 1996; Socrates 2001 ; Boeriu et al. 2004 ; Bruni and Guglielmi 2014 ; Cinta-Pinzaru et al. 2012 ; Monnier et al. 2017a ; Beltran et al. 2016 ; Chambre and Dochia 2021 ; Schmidt et al. 2023 ). When assessed alongside reference libraries, similarities to modern tree resins ( Pistacia and spruce) from both the Kimmel library and University of Minnesota Archaeological Materials Infrared Spectra Library, and rosin from University of Minnesota Archaeological Materials Infrared Spectra Library, were apparent in both the functional group and fingerprint regions. Group 3 (Fig. 6 ) is defined by a strong dominance of pine resin, either pure or in combination with additives like beeswax, charcoal, flax, or ochre. The spectra in this group are similar to groups 1 and 2 in the 3400 cm⁻¹ area, and have tightly clustered peaks in the functional group region at 2967, 2940, 2856 cm⁻¹ as well as a characteristic region of diagnostic peaks present at about 1735, 1695, 1600, 1515, 1435, 1375, 1280, 1235, 1130, and 1040 cm⁻¹. These peaks reflect resin-derived aliphatic and aromatic terpenoids and carboxylic acids (Edwards 1996; Edwards and Falk 1997 ; Socrates 2001 ; Mazza et al. 2006 ; Bruni and Guglielmi 2014 ; Helwig et al. 2014 ; Beltran et al. 2016 ; Monnier et al. 2017a ; Martin-Ramos et al. 2018 ; Schmidt et al. 2024 ), with possible modifications arising from thermal alteration (charcoal) or additives such as beeswax, flax, and ochre (Socrates 2001 ; Boeriu et al. 2004 ; Vahur et al. 2011 ; Diefendorf et al. 2015 ; Duce et al. 2015 ; Chambre and Dochia 2021 ; Das et al. 2024 ; Schmidt et al. 2024 ). Comparison to reference libraries yielded similarities to modern tree resins ( Pistacia and spruce) from the Kimmel Library and University of Minnesota Archaeological Materials Infrared Spectra Library, respectively. Group 4 (Fig. 7 ) encompasses all the proteinaceous glues: commercial and experimentally made sinew glue, bone glue, and hide glues. This group displays a very different spectral profile from the others, characterized by unique peaks at higher frequencies including at about 3330, 3080, and 2950 cm⁻¹, associated with O–H, N–H and C-H stretching vibrations. The dominance of amide I and II bands around 1650 and 1550 cm⁻¹ reflects the collagen-based nature of these materials, clearly setting this group apart in classification, along with phosphate signatures at about 1280, 1240, 1200, 1030, and 980 cm⁻¹ (Socrates 2001 ; Chadefaux et al. 2008 ; Vahur et al. 2011 ; Nunziante-Cesaro and Lemorini 2012 ; Helwig et al. 2014 ; Solodenko et al. 2015 ; Bradtmoller et al. 2016; Monnier et al. 2017b ; Monnier and May 2019 ). When compared to reference libraries, there were strong similarities to bone and hide glue from INFRA-ART’s library, as well as gelatin and leather from the Kimmel library. Similarities to silica were also present, indicating the high influence from the flint substrate. Of all groupings, Group 5 (Fig. 8 ) is the most heterogenous; chemically diverse but unified by the common use of spruce resin, beeswax, or acacia gum, with beeswax being the unifying element. Samples in this group often contain mixtures, such as spruce with beeswax or ochre, or acacia gum mixed with beeswax. Peaks at 2950, 2920, 2850, 1740, 1730, 1695, 1470, 1460, and 1240 cm⁻¹ are consistent across the group, likely corresponding to aliphatic and ester groups from beeswax, common lipids, as well as trace polysaccharide residues from gum (Edwards 1996; Regert et al. 2001 ; Socrates 2001 ; Martin-Ramos et al. 2018 ; Chasan et al. 2021; Das et al. 2024 ). The presence of both plant and animal-based components in various ratios accounts for the spectral complexity found here. Reference library comparisons yielded strong similarities to waxes, including beeswax (INFRA-ART) and paraffin (Kimmel), in both the functional group and fingerprint regions. Group 6 (Fig. 9 ) is represented by a single sample composed of pure acacia gum. This group has notable peaks at about 2930, 2880, 1610 − 1600, 1420, 1250 − 1230, and 1080 − 1040 cm⁻¹. The absence of strong protein or resin-associated peaks present in the other groupings results in a sparse spectral fingerprint, explaining its isolated position in analyst classification and PCA. Its unique polysaccharide signature may contribute to distinctive absorption features not widely shared with other groups beyond the brief appearance within Group 5, presented by the beeswax-acacia gum admixture (Edwards et al. 1996 ; Socrates 2001 ; Martin-Ramos et al. 2018 ; Thombare et al. 2023 ). In analyzing the classification results, five of the 31 samples emerge as notable outliers, either due to their hybrid compositions, unusual spectral signatures, or incongruities between their chemical makeup and assigned groupings. One such case is Sample 91 − 10, which contains a complex mixture of Pinus nigra , beeswax, and charcoal. While its substantial Pinus component would suggest alignment with Group 3, which includes other pinus-based adhesives, this piece was classified into Group 1 by the first analyst, with Group1 being typically dominated by spruce and mineral additives like ochre, sand, or clay. This may indicate that the presence of charcoal significantly altered the infrared absorption characteristics, drawing its peak profile closer to that of Group 1 materials. However, the classification by the second analyst placed 91 − 10 within Group 5, which coincides with its beeswax content. Similarly, Sample 91 − 14, composed of Pinus , beeswax, and ochre, is placed in Group 5 rather than Group 3. Like 91 − 10, its high beeswax and ochre content might mask or modulate the organic peaks typically associated with pine resins, resulting in a spectral shift and making it a transitional case between the two groups. Another ambiguous case is Sample 91 − 12, a blend of pine resin, beeswax, and flax. It shares a similar profile with several Group 3 samples, including 91 − 11 and 91 − 13, yet was initially categorized in Group 2. This suggests that the inclusion of flax, rich in plant fiber and possibly influencing ester and alcohol regions of the spectrum, may have introduced variability that aligned it more closely with other flax- or fat-containing mixtures typically found in Group 2 (Boeriu et al. 2004 ; Monnier et al. 2017a ; Chambre and Dochia 2021 ; Das et al. 2024 ). During the second classification, 91 − 12 was placed within Group 3, more closely aligning it with the other pine-dominant samples, with notable similarities to Group 5 in the functional group zone but with enough similarities to Group 3 between 1800 − 700 cm⁻¹ range to warrant its final placement. The shift in classification despite comparable base components points to the nuanced influence of additives on spectral clustering as well as the role of analyst choices. Thermal processing also appears to play a critical role in group differentiation. Sample 91 − 25, composed of spruce and beef fat subjected to frequent heating, is classed in Group 5, even though other similarly composed samples (91 − 22 and 91 − 24) are found in Group 2. The act of repeated heating may have chemically altered its composition through oxidation or carbonization resulting in distinguishable changes in the infrared spectrum, particularly in the C = O and C–H stretch regions (Duce et al. 2015 ; Schmidt et al. 2015 ; Diefendorf et al. 2021). Its classification divergence underlines the importance of considering preparation methods in FTIR-based adhesive analysis. Sample 91 − 06, a 1:1 mixture of acacia gum and beeswax, also occupies a liminal space. While its beeswax content aligns with Group 5, the presence of acacia gum introduces a polysaccharide-rich signature more characteristic of Group 6, which consists only of 91 − 05. The latter, composed of pure acacia gum, is chemically and spectrally distinct from all other samples. As such, 91 − 05 serves within this dataset as a clear outlier and a compositional benchmark for plant gum-based adhesive systems. It illustrates the strong differentiation that pure gum exhibits compared to resin, fat, or protein-based adhesives. These outlier samples highlight the fluidity and complexity of classifying archaeological adhesives of unknown compounds based on FTIR data. They underscore how both material composition and post-depositional or preparatory processes such as mixing or heating can influence spectral outcomes. At the same time, their identification draws attention to the role of grouping itself as an analytical strategy. The dataset analyzed here was not originally structured in terms of groups, but groupings were applied as a means of visualizing relationships and exploring variation, reflecting a broader interpretive choice to treat compositional similarities or differences as analytically significant. This underscores not only the importance of refining group definitions in specific cases, but also of critically reflecting on how such strategies shape future interpretive work on adhesive technologies. 3.c. Principal Component Analysis A core question addressed is whether PCA, as an exploratory tool for visualizing variability in spectral data, can reveal trends that align with groupings identified by analysts, and whether such methods would be transferable to archaeological adhesives. Statistical analysis techniques were incorporated to better visualize and interpret the spectral data, addressing potential limitations of visual classification which can be subject to analyst choice, bias and skill. These exploratory PCA results show that instrument-specific characteristics subtly influence the structure and effectiveness of the multivariate models, even despite sample composition and preprocessing steps remaining consistent. Both ZnSe/TE-MCT-A and KBr/LN-MCT-B datasets were preprocessed identically but yielded markedly different results in terms of group separation and explained variance. Adding a first derivative transformation to the ZnSe/TE-MCT-A dataset – a step that minimized the effect of baseline differences – substantially improved the clarity and compactness of class separation in PCA space, even though the raw spectra already exhibited fairly stable baselines. Rather than primarily correcting baseline drift, the derivative enhanced gradual absorbance trends and inflection points, converting subtle slope changes in the raw spectra into discrete spectral trends that aided in discriminating chemically similar adhesives. While SNV and GLSW alone partially corrected for scatter effects and sample variability, they were less effective in resolving overlapping adhesive classes. Importantly, while the ZnSe/TE-MCT-A spectra did not always suffer from severe baseline drift, the application of a derivative still enhanced discrimination by amplifying chemically meaningful slope features, particularly in overlap-prone regions. The resulting PCA plots demonstrate clear separation among all adhesive classes, with reduced intra-group dispersion and greater inter-group distances, especially between spruce-based, protein-based, and pinus-based adhesives. These improvements suggest that derivative preprocessing reveals subtle yet chemically meaningful spectral differences that are otherwise masked by baseline variation or low-frequency noise. Although the explained variance by the first PCs remains modest, this was not the explicit objective of preprocessing. An alternative strategy might have been to maximize explained variance by selectively removing spectral regions or tailoring preprocessing toward the first few PCs. However, the current approach shows that such steps were unnecessary – meaningful visual groupings and improved class interpretability were achieved without deliberately inflating variance capture. This supports the use of a first derivative as a beneficial preprocessing step for ZnSe/TE-MCT-A-based FTIR data in archaeological residue applications. To evaluate the discriminative power of FTIR data processed via PCA, two score plots were generated from the same preprocessed dataset: one labeled according to known adhesive composition, and the other based on analyst-assigned groupings without prior knowledge of adhesive types (Fig. 10 ). Both plots reveal at least some overlap among certain adhesives classes, particularly spruce-based, gum-based, and pinus-based mixtures, yet also show distinct separation for more compositionally unique adhesives. Protein-based samples exhibit clear clustering in both plots, while birch tar also forms a separate grouping, suggesting that these adhesives possess sufficiently distinct spectral signatures to be reliably identified via PCA. The analyst-defined approach allowed for exploratory assessment of natural clustering tendencies in the data, but the resulting plots showed substantial overlap and inconsistent boundaries, particularly among resin-based samples. By contrast, the adhesive-defined classification provided an objective framework more grounded in material composition and produced clearer separation for chemically distinct groups with mixtures clustering more tightly under their compositionally defined classes than under analyst-derived labels. Although both methods revealed meaningful structure, the adhesive-based groupings ensured consistent and reproducible class definitions, particularly important given the inclusion of composite adhesives with mixed chemical origins. These mixtures often produced spectra that spanned or blended characteristic features from multiple materials, complicating visually derived classifications. For this reason, the adhesive-defined model was selected for application to the validation set, as it offered a more transparent and chemically grounded basis for evaluating model performance and assessing the reproducibility of spectral patterns across instruments and analytical conditions. Constructing the training set using adhesive-defined classes that were based on the known chemical composition of each experimental residue offered significant advantages over relying on analyst-defined groupings derived from spectral inspection alone. Adhesive-based classification ensured that the grouping criteria were grounded in actual material properties rather than subjective visual interpretation, reducing the risk of bias and enhancing the model's validity. This approach also allowed for more accurate evaluation of spectral reproducibility and class discriminability, as each sample could be reliably assigned to a chemically distinct category. In contrast, analyst-defined groupings, while useful for exploratory clustering, often grouped samples based on dominant spectral features that did not always align with underlying primary compositional differences, especially in cases involving complex mixtures or overlapping functional groups. By anchoring the training set in compositional reality, the adhesive-defined model provided a more objective and interpretable framework for multivariate analysis, which is particularly important for future application to unknown archaeological residues. Applying PCA to both raw and KKT-processed data allows for a comparative assessment of how preprocessing influences the structure and interpretability of FTIR spectral datasets. PCA on raw spectra retains the full spectral signal, including baseline variation, reflectance effects, and instrument-specific noise, offering a realistic view of the inherent variability present in the original measurements. This is particularly useful for evaluating intra- and inter-instrument consistency, as well as identifying outliers or systematic differences due to sample presentation or acquisition conditions. In contrast, applying PCA to KKT-processed spectra emphasizes subtle peak shifts and shape differences while minimizing baseline drift and multiplicative effects. This enhances the ability to resolve chemically relevant features and differentiate between adhesive classes, especially those with overlapping spectral profiles. Together, these analyses provide a more comprehensive understanding of spectral variability; raw reflectance PCA reveals the challenges of direct interpretation under real-world conditions, while PCA on transformed reflectance spectra highlights the latent structure accessible through optimized preprocessing. Interestingly, PCA projections revealed that raw ZnSe/TE-MCT-A spectra produced clearer class separation than their fully preprocessed counterparts, whereas raw KBr/LN-MCT-B spectra required more extensive preprocessing to achieve meaningful clustering (Fig. 11 ). This contrast reflects underlying differences in spectral data quality between the two instruments. When unprocessed, ZnSe/TE-MCT-A spectra were generally high in signal-to-noise ratio and exhibited stable baselines, allowing key chemical features to be preserved even in minimally processed form. In this case, aggressive preprocessing steps such as derivative transformation and GLSW may have inadvertently suppressed or distorted class-relevant variation, reducing interpretability. Conversely, raw KBr/LN-MCT-B spectra were more affected by baseline shifts, scattering, and sample presentation artifacts, which obscured chemically meaningful patterns. Preprocessing, particularly SNV and GLSW, was therefore essential to reduce noise and emphasize diagnostic features, resulting in improved class clustering. These findings highlight the importance of instrument-specific preprocessing strategies and suggest that raw spectral quality should guide the intensity of data transformation applied prior to multivariate analysis. PCA of the raw, truncated spectra revealed distinct differences in how adhesive classes are distributed across the ZnSe/TE-MCT-A and KBr/LN-MCT-B datasets. In the ZnSe/TE-MCT-A PCA plot, adhesive groups such as protein-based, spruce-based, and pinus-based classes clustered clearly along PC1 and PC2, indicating that even without extensive preprocessing, the ZnSe/TE-MCT-A spectra retained strong chemically driven variance. This suggests a high baseline quality and signal stability inherent to the ZnSe/TE-MCT-A instrument, allowing compositional differences among adhesives to emerge naturally in PCA space. In contrast, the KBr/LN-MCT-B raw data exhibited more compression and overlap among adhesive classes, with only the protein-based and gum-based samples forming somewhat distinct clusters. The tighter clustering and reduced spread along both principal components suggest that KBr/LN-MCT-B spectra in raw form contain more baseline variation and scatter-related noise, which obscures fine-grained chemical distinctions. In contrast, when smoothed and KKT processed data were analyzed, KBr/LN-MCT-B spectra demonstrated improved separation between adhesive classes, while ZnSe/TE-MCT-A results were comparable or slightly less resolved. These results highlight the importance of developing a tailored preprocessing strategy that accounts for both the specific characteristics of the instrument and the nature of the samples being analyzed. In this study, preprocessing proved especially important for the KBr/LN-MCT-B-generated data, whereas the ZnSe/TE-MCT-A-generated data retained more interpretable chemical features even in their raw form. However, this outcome should not be generalized across all cases. Rather, it underscores the need for a flexible, iterative approach informed by testing against reference samples. Effective preprocessing is not one-size-fits-all, rather it depends on the optical configuration, detector type, and sample properties, and must be optimized accordingly. Expanding the KBr/LN-MCT-B dataset to include the lower wavenumber region (down to 400 cm⁻¹) had a modest but noticeable effect on PCA group structure (Fig. 12 ). Notably, the added inclusion of the 650 − 400 cm⁻¹ region did not drastically alter major groupings but appeared to refine within-group structure, particularly among plant resin classes (spruce- and pinus-based), which remained partially overlapping yet slightly more compact in the extended model. These results suggest that, at least for the KBr/LN-MCT-B data, the core chemical distinctions between adhesives are already well-represented in the 1800 − 650 cm⁻¹ region, but the extended range may help stabilize clustering and support classification by providing subtle inorganic-related features not captured above 650 cm⁻¹. These results suggest that while PCA can help visualize certain spectral trends, particularly in chemically distinct groups, it remains limited in resolving complex or compositionally overlapping mixtures without prior classification or expert intervention. Visual inspection remains essential as the most reliable method of identification and classification, and multiple spectra per sample are necessary for accurate identification due to the complex nature of composite materials as well as the presence of inclusions or contaminants on spectral reliability. PCA serves as a valuable first step in processing and visualizing trends in spectral data; however, it cannot replace expert interpretation. In this case, results remain somewhat ambiguous, emphasizing the need for alternative processing and subsequent analysis. HCA was conducted using both preprocessed spectral data directly and PCA-reduced data, allowing comparison of clustering outcomes under different dimensionality conditions. Preprocessing involved KKT transformation, truncation, smoothing, SNV correction, and GLSW weighting, with analyses of the ZnSe/TE-MCT-A datasets further incorporating a first derivative transformation. Across approaches, Ward’s method was employed as the linkage criterion to optimize the formation of compact clusters. When HCA was performed directly on the preprocessed spectra without PCA, the model produced a very detailed branching structure, splitting samples into many small sub-clusters. This captured subtle spectral differences but also created “clustering noise”, with closely related adhesives sometimes scattered across multiple branches rather than grouped together, particularly in samples with overlapping or composite compositions. In contrast, HCA on PCA-reduced data (using either 3 or 5 principal components) yielded more coherent and interpretable cluster structures, aligning more closely with known adhesive groupings such as spruce-based, pinus-based, proteinaceous glues, and gums. Notably, the application of the first derivative prior to PCA improved separation between clusters by enhancing slope-related spectral features, reducing within-cluster variance. Furthermore, subsampling the dataset by including every other sample or a single representative spectrum per sample helped minimize redundancy-driven clustering biases. This reduction in dataset density led to more distinct cluster boundaries, particularly in PCA-informed HCA (Fig. 13 ), where the variance-weighted distance between cluster centers increased, indicating stronger inter-group differentiation. Overall, PCA-informed HCA with 3 PCs offered a balanced approach, maximizing class separation while reducing noise, especially for complex adhesive mixtures. The resulting dendrograms showed a structure that broadly captured the expected relationships among adhesive types, demonstrating that this method not only reduced noise but also yielded chemically meaningful groupings 3.d. Validation Results The projection of the validation set which was composed of pure materials including beeswax, spruce resin, acacia gum, and pine resin into the existing PCA models reveals only partial consistency with the structure established by the training data (Fig. 14 ). Importantly, this validation test is not intended as a traditional accuracy check, but rather as a stress test to evaluate limits and behavior of the model when applied to fundamentally different data types. Through that lens, the results expose important limitations in classification confidence. A few validation samples, particularly those derived from spruce resin, do plot within or near their expected clusters, especially when applied to raw spectral data. However, the behavior of the other samples is inconsistent with what is expected assuming that the primary adhesive component governs the distribution of clusters within the PCA space. Here, pure pine resin clusters closely with the spruce-based adhesive group, a feature not seen in the training data, and pure beeswax appears near the pinus-based mixtures. While it is difficult to interpret with full certainty why these inconsistencies arise, these results suggest that the PCA model is not isolating the dominant component of each adhesive mixture as a distinct signal but is instead capturing spectral characteristics of shared ingredients or broader compositional similarities. For example, the overlap between pure beeswax and pinus-based adhesives may reflect the inclusion of beeswax in many of those mixtures, highlighting PCA’s tendency to amplify patterns tied to shared components rather than to isolate patterns related to individual substance types. More broadly, some pure validation samples occupy ambiguous positions between previously defined clusters or fall closer to material groupings that do not coincide with their known composition. The convergence of pure spruce and pure pine resins within the spruce-based adhesive cluster likely reflects chemical similarities between the two resins, reinforcing that PCA is sensitive to overall compositional resemblance. Notably, the mixed adhesive samples used for model calibration are more cleanly separated in PCA space than their pure counterparts, underscoring the model’s tuning toward composite patterns. These findings emphasize the limitations of PCA-based pattern detection when applied to chemically simple or unfamiliar inputs within the tested model. The presence of substantial misalignment, even within a controlled set of pure materials, raises concerns about the model’s capacity to reliably classify more complex archaeological residues. This analysis demonstrates that while PCA captures meaningful patterns, it does not reliably extract or differentiate individual components from within complex mixtures. Accordingly, classifications derived from PCA trends should be treated as suggestive rather than definitive, particularly in archaeological contexts where material composition is unknown, requiring validation through direct spectral comparisons, reference libraries, and complementary analytical methods. Without such safeguards, there is a risk of overinterpreting PCA plots and drawing unsupported conclusions from proximity alone, emphasizing the need for a cautious interpretive approach. The HCA dendrogram showed somewhat clearer structure. Both beeswax samples clustered together, as did the two acacia gum samples, suggesting that these pure substances maintain distinct spectral signatures under the applied preprocessing conditions. The pine resin samples formed a broader but coherent cluster, while the spruce resin samples were positioned adjacent to but slightly distinct from pine, reflecting expected chemical similarities yet allowing some differentiation. The clustering distance between gum, wax, and resins indicates that the model could broadly distinguish between major adhesive categories, although within-resin differentiation remained modest. The HCA was significantly more well defined when applied to the PCA-reduced data. As an added validation measure and to further evaluate the effect of sample orientation, spectra from rotated samples were also projected onto the ZnSe/TE-MCT-A PCA model. The PCA results (Fig. 15 ) revealed distinct clustering patterns consistent with prior analyses but also highlighted the influence of both orientation and composition. All rotated spectra from sample 91 − 11 formed a tight and isolated cluster on the far right of PC1, demonstrating that even highly orientation-sensitive samples may cluster consistently in PCA space, suggesting that the dominant variation lies outside the primary principal components. Most spectra from spruce-based samples, especially 91 − 26, 91 − 28 and 91 − 29, clustered tightly regardless of orientation, reinforcing their high intra-sample reproducibility. However, samples 91 − 30 and 91 − 32 exhibited notable internal dispersion. While some spectra from each sample aligned closely with their respective clusters, other spectra from the same sample were displaced along PC1, indicating subtle but meaningful variation across orientations. This intra-sample spread suggests that microstructural or compositional heterogeneity, possibly due to layering effects, clay particle alignment, or other interactions between the matrix and additives, introduced variation detectable by PCA despite identical adhesive composition. These findings emphasize the importance of considering both sample orientation and material composition when interpreting clustering patterns in multivariate FTIR analysis. 3.e. Comparison of Reflectance-mode to Transmission-mode Spectra Here, reflectance-mode FTIR successfully enabled the detection of key spectral features in the experimental adhesives, particularly after KKT. Once transformed, the spectra exhibited strong alignment in peak position with corresponding transmission-mode spectra from external reference libraries, specifically in the fingerprint regions, supporting their comparability for qualitative analysis. This provides further evidence that with appropriate processing, spectra collected in reflectance-mode can be meaningfully interpreted using transmission-based reference standards. Despite this success, spectral quality in the reflectance spectra remained lower overall, even with the incorporation of 400 co-added scans per spectrum, likely due to the influence of surface roughness, matrix heterogeneity, and other optical effects. Important for further considerations is that most established reference libraries are built from transmission-mode data, which is typically collected on pure compounds under controlled conditions and without the constraints of non-destructive sampling requirements. This presents a challenge when interpreting reflectance-mode data from complex, heterogenous residues on archaeological materials. In the study here, while only a few exact spectral matches were found in existing libraries likely due to the composite and variable nature of the sample materials, major peaks allowed for moderate-level classification of adhesive types such as resins, waxes, gums, and proteinaceous materials through visual grouping (Fig. 16 ). These results underscore the need for additional experimental reference materials, particularly those prepared on lithic substrates, to improve identification accuracy and increase the interpretive value of non-destructive FTIR in archaeological applications. 4. Discussion 4.a. Spectral Reproducibility Instrumental variation is a well-known but little addressed challenge in FTIR spectroscopy, particularly when comparing reflectance-mode data across platforms with differing optical hardware. Notably, KKT without normalization yielded the best performance in intensity-based metrics. These results support the application of KKT and amplitude normalization in cross-instrument studies and underscore the need for transparency in processing choices. Results further support the viability of FTIR as a successful method for interpreting various organic materials in the archaeological record, particularly as related to adhesive materials. The variations encountered in spectral uniformity are to be expected to an extent as a result of instrument specific differences, but are, for the most part, either minor or in part remedied through processing techniques which result in spectra that can be cross-referenced without difficulty. This study also supports the need for extensive and accessible reference libraries as a field-wide standard to increase the reproducibility of testing and ensure materials are being reliably classified. While minor variation in peak location was observed between instruments, it was usually not far outside of the expected range. It is possible that the observed variation in peak location and intensity could be related to minor degradation of the material over time, suggested by the high rate of occurrence within the OH and CO groups, though this could not be isolated and tested for through this study and poses an opportunity for further exploration. The limited spectral range of the ZnSe/TE-MCT-A as currently equipped (ending at 650 cm⁻¹) is one of the most significant constraints, especially for materials that present diagnostic peaks in the lower region of the mid-IR spectrum, particularly inorganic additives or environmental contaminants, such as was the case for samples classed into Group 1. When standardized processing techniques are applied consistently across instruments, the reproducibility of data is enhanced but still presents some variability. This suggests that with appropriate processing, FTIR results are more transferable across platforms, supporting the broader comparability of adhesive residue analyses and facilitating collaborative, multi-institutional research while emphasizing the urgency of establishing standardized protocols for such comparisons and the importance of explicitly outlining spectral processing steps. The findings of the orientation reproducibility test underscore the significant influence of surface topography and compositional anisotropy on FTIR spectral stability, particularly when measurements are collected in reflectance mode. As presented here, the optical properties of a sample’s surface, particularly its micro-topography, refractive index variation, and alignment of constituents based on shifted orientation on the measurement platform, can introduce detectable variability in spectral outcomes, even when instrumental parameters are held constant. Interestingly, samples such as 91 − 26 (spruce resin with ochre) and 91 − 29 (spruce resin with sand) exhibited minimal spectral variation across orientations. In this case, these samples present visually even particulate coatings, resulting in relatively uniform reflection with limited dependence on beam angle even with the presence of mineral-based additives. The mineral additives in these samples, including finely ground ochre and fine sand, show largely isotropic and static behavior within the surrounding matrix, despite their crystalline structure, resulting in the stable scattering profiles and absorbance signals that are observed (Coats et al. 2003 ). The reflectance behavior of the crystalline additives, which, according to Izzo et al. ( 2020 ), is largely governed by surface roughness and refractive index variability, appears to be evenly distributed within the adhesive matrix across orientations in these cases, resulting in minimal variation between the spectra. Conversely, sample 91 − 11 (pine resin with flax fiber) showed substantial orientation sensitivity. Flax, a structured and fibrous organic inclusion, introduces anisotropic scattering due to its elongated morphology and directional grain of the filament. In reflectance FTIR, incident light interacts not only with the surface but also with microstructural angles (Coats et al. 2003 ; Izzo et al. 2020 ); as noted by Van Nimmen et al. ( 2008 ) and Belbachir et al. ( 2011 ), fibers aligned parallel to the beam may scatter differently or present different path lengths than those perpendicular to it. These interactions can lead to measurable reflectance shifts, which alter both spectral intensity and peak shape (Mercurio et al. 2018 ; Izzo et al. 2020 ). The resulting spectra showed poor correlation, high variability and large effect sizes clearly indicating that fiber orientation governs spectral outcomes in this case. Intermediate cases were observed in samples like 91 − 28 (spruce resin with ochre and clay), 91 − 30 (spruce resin with clay), and 91 − 32 (spruce resin with beef fat and clay), which demonstrated largely reproducible spectra with occasional shifts in baseline or peak sharpness. These minor deviations may stem from subtle surface layering or light trapping effects due to uneven spreading of crystalline additives within matrix films, which can introduce slight differences in reflectance interactions introduced by crystal orientation, (Mercurio et al. 2018 ; Izzo et al. 2020 ). Still, their overall reproducibility suggests that compositional homogeneity at the micro-scale may be sufficient to limit orientation-based variability. Projecting orientation-shifted spectra into the PCA model provided insight into how both composition and microstructure can influence spectral classification. Spectra from 91 − 11, despite showing strong orientation effects in pairwise tests, clustered tightly in PCA space, suggesting that orientation-induced changes did not significantly affect the dominant chemical variance captured by the model. In contrast, samples 91 − 30 and 91 − 32 showed greater dispersion, likely reflecting compositional complexity and microstructural heterogeneity, such as uneven clay distribution or interactions between fat and mineral phases. Meanwhile, 91 − 28 and 91 − 29, which contain simpler or more homogeneously distributed additives, remained tightly grouped. These results demonstrate that PCA can capture variation introduced by both chemical composition and physical structure, factors that must be considered in archaeological applications where residues are often heterogeneous and degraded. These results highlight the importance of understanding how microstructural and topographic features influence IR reflectance, a factor that should not be overlooked in residue or artifact surface analyses. Spectral differences introduced by orientation are not necessarily indicative of chemical change, but rather of optical path length variation, scattering geometry, and local refractive behavior. For materials with directional structure, such as plant fibers, layered adhesives, or composite residues, careful sample mounting, surface flattening, or use of transmission or ATR modes may be required to ensure data accuracy and comparability. In reflectance-mode fieldwork or heritage contexts where control over sample presentation is limited, orientation effects should be explicitly tested and, if necessary, normalized or excluded from quantitative comparisons. Ultimately, these findings support that spectral reproducibility is not solely a function of instrument fidelity, but also of material surface behavior under IR illumination. Residue analysis protocols should therefore consider orientation tests as a part of methodological validation, particularly when dealing with organic or composite surfaces whose topography may distort reflectance behavior. To improve the reliability of reflectance FTIR-based residue interpretation, we recommend sampling a minimum of three to five distinct points on a single sample surface, where preservation permits. This approach helps capture intra-sample variability and reduces the risk of basing interpretations on localized contamination or spectral anomalies. It is also advisable to target macroscopically visible residues and adjacent areas to obtain a more comprehensive chemical profile, which can strengthen confidence in material identification. Additionally, sampling areas of the lithic substrate without the suspected residues present provides an important background reference, offering insights into the potential influence of the underlying material and any adhering sediments from burial. These background spectra are critical for distinguishing authentic residues from post-depositional contamination and for understanding how the substrate and burial conditions may affect the resulting spectra of perceived residues. 4.b. Classification Challenges and Interpretive Nuances This study demonstrates both the promise and limitations of FTIR spectroscopy for identifying organic adhesives in archaeological contexts, particularly when addressing composite materials or comparing data derived from reflectance-mode collection to existing transmission-mode standards. The successful alignment of major peaks between reflectance-mode spectra (following Kramers-Kronig correction) and transmission-mode spectra underscores the viability of reflectance-mode FTIR, particularly when destructive sampling is not permitted. However, the higher spectral noise observed in reflectance-mode spectra compared to transmission-mode is likely to be exacerbated in archaeological materials, presenting potential challenges in successful material identification. The possibility of minor shifts in peak position as a result of instrumental differences should also be considered, especially when analyzing archaeological materials which are subject to increased compositional variation as a result of degradation, taphonomic influence, diagenetic processes, and other possible points of contamination from environmental sources or from material handling. Visual grouping of spectra by analysts proved effective for initial classification to distinguish adhesive types including gums, plant resins, tars, waxes, and animal-derived glues. However, visual interpretation is subject to analyst experience and requires extensive knowledge. To mitigate this, chemometric methods including PCA and HCA were tested. While PCA did accurately reflect visually defined clusters, particularly for more chemically distinct groups, clear trends were not always apparent without prior classification input. For example, in Fig. 12 the spruce-based group and birch tar group are somewhat poorly separated in PCA space. This can be seen even more within the validation set when projected onto calibration data, such as in Fig. 15 , where we see pure pinus samples overlapping with the spruce-based group and pure beeswax overlapping with the pinus-based group. These results suggest that PCA is useful for visualizing spectral trends in chemically distinct samples, but it remains limited in resolving more complex or overlapping composite mixtures. Moreover, the PCA outcomes highlight a key challenge in spectral interpretation, being that instrumental artifacts influence variance and can obscure meaningful chemical differences, even with rigorous preprocessing. For the ZnSe/TE-MCT-A dataset, KKT-processed spectra captured relatively little variance in the first PCs, with a wide margin of variance still left in the residuals. By contrast, the raw data retained more variance in the modeled space and showed better agreement between calibration and validation, though the loadings remained relatively noisy. The KBr/LN-MCT-B dataset revealed the opposite pattern, with KKT preprocessing producing stronger separation and more stable distinctions when considering loadings, while the raw spectra were less consistent. When comparing the recipe-based and analyst-defined groupings, the underlying PCA space remains identical, yet the visual outcomes differ significantly: adhesive-defined classes highlight coherent, compositionally meaningful clusters, such as the clear separation between spruce- and pinus-based resins and the tight clustering of beeswax, birch tar, and protein-based adhesives. By contrast, analyst-driven groupings obscure these patterns in PCA space, merging spruce and pinus resins into partially overlapping clusters and scattering resin-wax mixtures across multiple groups. These contrasts emphasize that interpretive outcomes are shaped as much by classification frameworks as by instrument platform, preprocessing choices, and chemistry itself, underscoring the importance of transparency when presenting PCA-based residue studies. While each dataset shows internal structure and reflects meaningful patterning, the variance captured is fragile, and much of the signal remains difficult to interpret. This outcome suggests that although each instrument captures meaningful group differentiation, the nature of that differentiation is not directly comparable between platforms. While intra-instrument PCA models are reliable for exploratory analysis, inter-instrument comparability remains limited without advanced harmonization strategies. The models used here do capture real signal variation, exhibited by the group structures observable in each case, which provides a defensible foundation for class discrimination, outlier detection, and future supervised modeling. These results emphasize the importance of transparency in preprocessing documentation and reporting, robust cross-validation, and cautious interpretation when integrating chemometric results across platforms. These differences highlight the need for instrument-specific modeling strategies, as models optimized for one platform likely will not translate effectively to another due to differences in detector sensitivity, spectral resolution, and reflectance behavior. For robust classification and cross-instrument comparability, it is essential to tailor preprocessing, model construction, and validation procedures to each system’s signal properties. These findings collectively reinforce the methodological importance of both platform-aware preprocessing and model customization, particularly in archaeological FTIR applications where reproducibility and interpretive accuracy are critical. The clustering outcomes from HCA accentuates the critical influence of both preprocessing strategy and dimensionality reduction on the resolution and reliability of spectroscopic classifications. Performing HCA directly on preprocessed spectral data retained fine-scale spectral variation but often resulted in over-clustering or fragmentation of chemically similar adhesives, likely due to the high dimensionality and residual noise in the dataset. By contrast, integrating PCA prior to clustering helped streamline the data structure, concentrating on the most informative variance while suppressing minor fluctuations that do not contribute meaningfully to chemical differentiation. The choice of three to five principal components was particularly effective, as it captured the majority of chemically relevant variance without incorporating noise-dominated components. The improved clustering performance in the reduced and subsampled datasets also suggests that redundancy in spectral replicates can obscure class boundaries, emphasizing the value of representative sampling in spectroscopic studies. These findings highlight the importance of tailoring multivariate approaches to the specific characteristics of the dataset and instrumentation. For archaeological residue analysis, where samples are often limited and compositionally complex, combining robust preprocessing, dimensionality reduction via PCA, and thoughtful clustering algorithms like Ward’s method can substantially improve the interpretability and accuracy of material classification. This integrated approach also supports the development of scalable analytical pipelines adaptable to larger and more variable archaeological datasets. The outcomes of the validation test complicate our understanding of how PCA-based models function in residue classification. While the models do capture meaningful patterns in spectral data, the misalignment of pure validation samples, particularly those that cluster unexpectedly or ambiguously, highlight both the strengths and the limitations of this approach. That even well-preserved, compositionally simple substances such as pure resins and waxes do not consistently align with their expected adhesive classes reveal the extent to which PCA may be influenced by the specific makeup of the calibration dataset. In this case, the training model seems to have captured overlapping spectral signals arising from shared ingredients rather than cleanly distinguishing each material based on its dominant component. This effect is particularly evident in the clustering of pinus resin within the spruce-based cluster and the proximity of beeswax to our pinus-based group. This may indicate that PCA is organizing the samples based on global spectral similarity rather than isolating distinct chemical signatures. The relatively tight clustering of the mixed adhesives compared to the pure materials further supports this interpretation, indicating that the PCA model may be better tuned to capturing composite patterns than identifying discrete differences in source materials. Inspection of the loadings reinforces this view – even in the leading components, the profiles appear noisy, with more subtle variation confined to later components or residuals. In the ZnSe/TE-MCT-A data, this resulted in less stable grouping under KKT preprocessing and better performance on raw spectra, while the KBr/LN-MCT-B showed the opposite trend. In the score plots, adhesive-defined classes in the training set separate more clearly than analyst-defined classes; proteins, birch tar, and beeswax form compact clusters, while spruce and pinus resins, though distinct in the training set, overlap when tested on validation data. This pattern is consistent with the loadings, where diagnostic resin-specific C-O stretching (~ 1100 − 1000 cm⁻¹) and beeswax CH₂ deformation (~ 1450 cm⁻¹) are emphasized in later components (PC4-PC5) but underweighted in PC1-PC2, which instead capture broad overlapping features that dominate variance across both pure substances and mixtures. Together, these results show that mixtures may cluster tightly because their broad variance patterns align with the dominant PCs, while pure substances misalign when their distinctive features fall into later components. Just as importantly, they demonstrate that outcomes differ systematically between instruments, meaning preprocessing strategies cannot be assumed to transfer uniformly across platforms, further stressing the importance of transparent reporting practices. These findings have significant implications for archaeological applications. Residues recovered from artifacts are rarely pure and often heavily degraded, mixed, or contaminated, making them significantly more complex than the validation samples used here. If the model cannot clearly resolve known, pristine substances, it will likely be challenged in detecting definitive differences in archaeological adhesive recipes. Instead, the PCA model should be seen as a tool for exploratory pattern recognition, helping to identify samples with broadly similar compositions, suggesting potential strategies for further investigation, and flagging samples of potential interest for more targeted or even destructive analyses. Visual inspection of spectra remains critical and will offer clearer insights into spectral similarity than multivariate analysis alone. To strengthen archaeological applicability, future work must expand experimental reference datasets, incorporate experimentally degraded or mixed adhesives, and develop multimodal analytical protocols that integrate FTIR with complementary techniques such as use-wear analyses and GC-MS. Moreover, rethinking the design of calibration datasets to better reflect the types of residues we expect to encounter in archaeological contexts will help ensure that PCA or HCA models offer meaningful and realistic interpretive scaffolds. Without these advancements, the current model risks oversimplifying the interpretive space of archaeological residues, potentially obscuring rather than clarifying the technological behaviors we seek to reconstruct. Substrate interference remains a concern in both spectral data collection and interpretation, often introducing noise or misrepresenting spectral features. In such cases, fingerprint regions can be variably impacted by substrate, particularly when residue deposits are thin or cover a small area, emphasizing the need for controlled background subtraction and the inclusion of blank lithic spectra in future archaeological reference libraries. While residue thickness, opacity, and roughness can generally be assessed visually under a microscope, such observations rarely eliminate the problem. Residues are often preserved in such small quantities that even with a very small aperture (e.g., 100 × 100 µm), it is not possible to fully isolate them from the underlying lithic or associated sediments when collecting FTIR measurements. In addition, surface topography exerts a strong influence on spectral quality, as archaeological residues are rarely flat and uneven surfaces can distort absorbance intensity or introduce scattering effects. As a result, interfering signals should be expected, whether or not residues appear opaque under magnification. Such interferences, compounded by degradation processes over time, highlight the limitations of comparing ancient samples directly with fresh modern references. To mitigate some of these challenges, multiple spectra per sample should be collected from different points across the residue deposit, which would require a significant increase in sample and scan numbers and may not always be feasible. Despite these challenges, the study’s visual classification strategy revealed meaningful compositional groupings, reflecting variation in base adhesives and additives. Several samples presented as outliers or transitional types, illustrating the substantial spectral impact of additives and preparation techniques, such as thermal processing. Specimens containing similar base materials were sometimes grouped differently due to their unique additive profiles or signs of repeated heating, which may chemically alter their spectral signatures. While the experimental mixtures reflect a broader range of material types and combinations than typically expected in a single archaeological context, the diversity was intentional to test the limits of the attempted classification methods under complex and potentially overlapping conditions. As such, some of the complications encountered within this study may not be as extreme when applied to true archaeological assemblages. Regardless, these nuances are important in archaeological interpretation, where understanding the technological choices of past people, including mixture recipes or heat treatment strategies, relies on accurate residue classification. The classification scheme explored here provides a framework for characterizing complex adhesive formulations, with each group reflecting distinct chemical profiles, while overlapping features in some samples underscore the inherent fluidity of real-world adhesive technologies. 4.d. Archaeological Applications and Interpretive Value Reflectance-mode FTIR serves as a valuable non-destructive step within an integrated approach to identifying glues on archaeological tools by first detecting the presence of organic residues and then characterizing their molecular composition based on diagnostic absorption bands. Adhesives such as birch tar, conifer resins, and plant gums exhibit unique spectral features that help distinguish them from other plant-derived residues. To move from detection to confident identification, these spectral signatures must be compared against well-curated experimental reference datasets that include both fresh and degraded forms of known adhesives. However, interpreting these results reliably also requires ruling out alternative sources of similar compounds, such as naturally occurring plant exudates encountered during woodworking or incidental contact with resinous woods as well as contextual data that relates the residues to tool function. Because spectral similarities can exist between some adhesive types and incidental residues introduced through other means, a multi-proxy approach is essential for strengthening interpretations. Use-wear analysis can provide independent evidence of how a tool was used, revealing patterns of wear consistent with hafting or adhesive application. Functional analyses can further clarify whether residues align with expected patterns from tasks like cutting, scraping, or drilling. When combined, chemical data from FTIR and morphological evidence from use-wear studies provide a more robust framework for interpreting the presence and function of adhesives in archaeological contexts. This integrated strategy mitigates the limitations of any single method and helps ensure that interpretations of prehistoric adhesive use are both chemically and behaviorally grounded. The methodological approaches presented in this study offer significant, though cautioned, potential for application to archaeological materials. The ability to generate reproducible and interpretable FTIR reflectance spectra across different instruments strengthens the reliability of non-destructive residue interpretation, which is a critical step toward integrating FTIR as a standard in sampling of archaeological residues where destructive techniques such as GC-MS are restricted due to preservation ethics or limited sample availability. The experimental adhesives analyzed here were selected to simulate a broad range of materials found in prehistoric hafting technologies The groupings and classification criteria developed provide a framework for interpreting similarly complex residues found on archaeological tools, offering preliminary reference signatures that may be useful for identifying material classes even when exact compositional matches are unavailable. Importantly, the study highlights how variations in adhesive composition, such as the inclusion of fat, ochre, or thermally altered additives, can significantly alter FTIR spectral profiles which can reflect meaningful differences in technological practice. The spectral distinctions identified here between different mixtures and processing techniques (such as repeated heating) could be used archaeologically to infer variability in craft traditions, standardization of recipes, or regional knowledge transfer. Beyond individual spectra, the comparative modeling and classification techniques explored here, particularly PCA and visual groupings, offer scalable strategies for handling archaeological datasets. Although the validation set employed in this study consists of experimentally controlled replicates, future work may substitute this component with spectra obtained from archaeological sources. In such cases, the experimentally derived adhesive references would continue to serve as the training set, providing a structured baseline against which unknown archaeological residues can be assessed. While the inherent complexity and degradation of archaeological materials may reduce classification confidence, the defined groupings and chemometric thresholds developed here can remain a useful guide for interpreting patterns of spectral similarity and divergence. Additionally, this framework provides a step toward refining machine-learning approaches to residue classification as larger and more diverse archaeological datasets become available. Further consideration concerns the visibility and thickness of residues on archaeological tools. While fully transparent adhesives like acacia gum are not common in the archaeological record, thin, minute, degraded, or weathered residue layers are frequently encountered. The spectral challenges presented by transparent substances in this study, especially in distinguishing their signals from lithic substrates, thus share important overlap with real-world archaeological conditions. Like transparent experimental residues, small archaeological deposits often exhibit low signal intensity, high noise, and substrate-dominated spectra. These similarities suggest that the methodological strategies outlined here, particularly the use of multiple sampling points, careful background subtraction, and substrate-matched reference spectra, are directly transferable to archaeological contexts. In this sense, even if exact material matches are not always available, the experimental findings remain crucial for informing data quality assessment, error margins, and interpretive caution in archaeological FTIR studies. This study also underscores several caveats essential to archaeological interpretation. Spectral quality of a residue is influenced by thickness of the deposit, transparency, and the reflectance properties of the underlying lithic substrate, residue surface, and inclusions within the residue itself. These conditions, common in archaeological contexts, can cause substantial interference, skewing spectral results and complicating interpretation. Additionally, while visual classification by analysts remains a powerful tool, its subjectivity poses a limitation in archaeological cases where relevant reference data are incomplete or inaccessible. The use of chemometric approaches such as PCA and cluster analysis, while still limited in resolving all material classes, may offer a somewhat standardized supplement to visual classification in detecting patterns within assemblages. These methods provide a potential route for handling large datasets or blind testing of unknown archaeological residues, especially in collaborative or multi-institutional research settings. Looking forward, the reproducibility demonstrated here between instruments, and the clarity of material groupings among experimental datasets, suggest several promising avenues for archaeological application. One such avenue is to serve as a screening method to prioritize samples for destructive analysis like GC-MS. Taking the materials here as a case study, several samples could reasonably be excluded from further analysis based on near exact reference matches (such as pure beeswax and pure acacia gum), prioritizing more compositionally complex samples such as birch tar and tree resin-based mixtures. Extending the example further, if destructive analysis were permitted on only three samples from this set, we might select samples 91 − 07 (birch tar), 91 − 23 (pine resin and fat mixture), and 91 − 27 (spruce resin, ochre and sand mixture) based on the outcome of the visual classification and comparison with reference spectra combined with PCA and HCA. These represent cases with ambiguous or absent reference matches where compositional overlap complicates classification and where complementary chemical analysis would most improve interpretive certainty. Other potential applications include classification of unknown archaeological residues into generalized material classes (resinous, proteinaceous, waxy, etc.) to support behavioral interpretation, assessment of adhesive degradation by comparing archaeological spectra to modern reference sets as well as simulated taphonomic alteration, and identification of technological variability across space and time, such as shifts in adhesive recipes or the introduction of new ingredients, which may reflect cultural transmission or environmental adaptation. Ensuring data availability is essential for advancing residue studies in archaeological science, particularly when addressing questions of reproducibility and methodological consistency. By making both raw and processed FTIR spectra, along with associated metadata, openly accessible, we enable other researchers to critically evaluate, replicate, or extend our findings. This transparency not only fosters scientific rigor but also facilitates further cross-instrument and cross-laboratory comparisons, which are necessary for establishing broader analytical standards in residue detection and advancing the progression of reflectance FTIR in archaeological applications. In the context of ongoing debates surrounding the reliability of biochemical residue identification, open data serves as a critical resource for improving methods and refining interpretations within the field. By bridging experimental and archaeological research, this study contributes not only a base-level methodological toolkit for improving FTIR reproducibility, but also a conceptual framework for interpreting adhesive technologies in archaeological contexts. Through careful calibration, comparison, and classification, FTIR residue analysis has the potential to shed light on the cognitive, technological, and cultural dimensions of prehistoric tool manufacture when strategically applied. 5. Conclusion The results presented here affirm that reflectance-mode FTIR, particularly when paired with strong reference libraries and thoughtful statistical approaches, can be a powerful tool for identifying unknown and potentially ancient adhesives. This study demonstrated improved spectral comparability across KBr/LN-MCT-B and ZnSe/TE-MCT-A platforms through standardized processing techniques, though gains in alignment came with some distortion of spectral shape, emphasizing the need to consider the effects of spectral processing on overarching goals, as well as the need to balance quantitative normalization with the preservation of diagnostic features. In addition to instrument variability, analyst decisions, classification strategies, and preprocessing choices were shown to influence interpretive outcomes. Visual classification and chemometric modeling each contributed valuable insights, but both require transparent criteria and representative training data to ensure consistent, transferable results. Chemometric tools such as PCA and HCA proved useful for visualizing group cohesion and assessing the internal consistency of classifications, especially in datasets with overlapping spectral features. These findings emphasize the importance of reproducible workflows and reporting practices to enhance comparability and collaborative potential across laboratories. It also reinforces the broader viability of reflectance FTIR as a transferable, reproducible tool for archaeological residue analysis, particularly when destructive sampling is limited or prohibited. Ultimately, this study supports a multi-method approach to adhesive residue analysis. While reflectance-mode FTIR provides rapid, non-destructive compositional data, expert interpretation, visual inspection and complementary analytical techniques remain essential, but each come with a suite of challenges. Continued expansion of reference libraries, along with further testing of degradation pathways and substrate interactions and development of robust, transferable methodologies, will enhance the accuracy, consistency and interpretive reliability of FTIR-based residue analysis in archaeological research. Declarations Declarations The authors declare that they have no competing interests. Author Contribution All authors contributed to the study design. Data collection and analysis were performed by L.L. and S.M. Figures were produced collaboratively by L.L. and S.M. The first draft of the manuscript was written by L.L., and all authors commented on previous versions. All authors read and approved the final manuscript. Acknowledgement The funding for the purchase of the Agilent KBr/LN-MCT-B system was provided by a Deutsche Forschungsgemeinschaft (DFG) grant to Christopher Miller (MI 1748/1–1). The license for the PLS Toolbox software is hosted in the University of Tübingen with funding for its annual maintenance provided by the Leibniz Association via the Geogenomic Archaeology Campus Tübingen (GACT). The funding for the purchase of the Bruker LUMOS II ZnSe/TE-MCT-A system was provided by a F.R.S.-FNRS equipment grant to Veerle Rots. The license for the Peak Spectroscopy software is supported by the GLUE project funded by the University of Liège (Collaborative Research Actions – ARC). All experimental materials were produced at the University of Liège by Christian Lepers, experienced primitive technologist. Veerle Rots is indebted to the FNRS (RD), Lauren Lien to the University of Liège (ARC). Data Availability The datasets generated and analyzed within this study are available in the ULiège Dataverse repository at https://doi.org/10.58119/ULG/BVGFPW. Naming conventions for each dataset are described in Supplementary Information, Table 1 (ST1). R scripts used for data processing and analysis are openly available on GitHub at https://doi.org/10.5281/zenodo.17160767 References Adam, H., Siddig, M. A., Siddig, A. A., & Awad Eltahir, N. (2013). Electrical and optical properties of two types of gum arabic. Sudan Medical Monitor , 8 (4), 174–178. Adler, H. H., & Kerr, P. F. (1965). Variation in infrared spectra, molecular symmetry and site symmetry of sulfate minerals. American Mineralogist , 50 , 132–147. Aleo, A., Jerardino, A., Chasan, R., Despotopoulou, M., Ngan-Tillard, D. J. M., Hendrikx, R. W. A., & Langejans, G. H. J. (2024). A multi-analytical approach reveals flexible compound adhesive technology at Steenbokfontein Cave, Western Cape. 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1","display":"","copyAsset":false,"role":"figure","size":709457,"visible":true,"origin":"","legend":"\u003cp\u003eSampling conditions under reflected light. Left\u003cstrong\u003e:\u003c/strong\u003e Analytical area with fully opened aperture, showing a low-topography sample (91-07) with most of the surface in focus (600 × 450 μm). Middle\u003cstrong\u003e:\u003c/strong\u003e Analytical area with fully opened aperture, showing a high-topography sample (91-06) where only part of the surface is in focus, with a visible inclusion (600 × 450 μm). Right: Analytical area with reduced aperture focusing on an inclusion in sample 91-29 (aperture ≈ 210 × 110 μm; full view 600 × 450 μm).\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7711776/v1/6d621ac5eaf30ba2ad4f7118.png"},{"id":92569961,"identity":"6bca4369-0820-446c-918e-bba20b667856","added_by":"auto","created_at":"2025-10-01 07:31:47","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":149379,"visible":true,"origin":"","legend":"\u003cp\u003eSummary of final preprocessing steps for each dataset and type of multivariate analysis presented as a set of flowcharts. The workflow for KBr/LN-MCT-B data is shown on top and ZnSe/TE-MCT-A data on bottom.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7711776/v1/31ad4bbbaa01834aa8a0183b.png"},{"id":92569966,"identity":"a01d22f8-1805-4e76-b1d5-079140da01a1","added_by":"auto","created_at":"2025-10-01 07:31:47","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":592570,"visible":true,"origin":"","legend":"\u003cp\u003eComparative FTIR spectra of samples 91-05 (top), 91-19/91-34 (middle), and 91-27 (bottom) acquired on the ZnSe/TE-MCT-A instrument (green) and the KBr/LN-MCT-B instrument (purple). The overlays illustrate subtle spectral variations between instruments, highlighting differences in baseline, peak intensity, and resolution while maintain overall profiles across platforms.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7711776/v1/687a026309a3f9b91194e44c.png"},{"id":92569976,"identity":"95d4d4e8-85f3-4b47-ba80-0206e73a3e84","added_by":"auto","created_at":"2025-10-01 07:31:48","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":474864,"visible":true,"origin":"","legend":"\u003cp\u003eOverview of analyst-defined spectral grouping classification for Group 1, comparing data from the KBr/LN-MCT-B (top) and the ZnSe/TE-MCT-A (bottom), showing subtle visual variation trends in spectra between the two instruments. KKT has been applied to these spectra.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7711776/v1/79a59b03d3f9f1b6da6dbf65.png"},{"id":92570813,"identity":"791894b5-b0b0-4d1b-8d15-326cfff4162e","added_by":"auto","created_at":"2025-10-01 07:39:50","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":509358,"visible":true,"origin":"","legend":"\u003cp\u003eOverview of analyst-defined spectral grouping classification for Group 2, comparing data from the KBr/LN-MCT-B (top) and the ZnSe/TE-MCT-A (bottom), showing visual variation trends in spectra between the two instruments. KKT has been applied to these spectra.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-7711776/v1/aa126dd89f51c1acd409ddef.png"},{"id":92569967,"identity":"46378200-913e-4362-8f6b-5de491e382db","added_by":"auto","created_at":"2025-10-01 07:31:47","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":508269,"visible":true,"origin":"","legend":"\u003cp\u003eOverview of analyst-defined spectral grouping classification for Group 3, comparing data from the KBr/LN-MCT-B (top) and the ZnSe/TE-MCT-A (bottom), showing subtle visual variation trends in spectra between the two instruments. KKT has been applied to these spectra.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-7711776/v1/b6597f42bc93a68fa39ea468.png"},{"id":92569964,"identity":"b957cab3-e6e3-41a2-9d6a-7611705ae1b6","added_by":"auto","created_at":"2025-10-01 07:31:47","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":562030,"visible":true,"origin":"","legend":"\u003cp\u003eOverview of analyst-defined spectral grouping classification for Group 4, comparing data from the KBr/LN-MCT-B (top) and the ZnSe/TE-MCT-A (bottom), showing subtle visual variation trends in spectra between the two instruments. KKT has been applied to these spectra.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-7711776/v1/f0b1961039ba74de48be5403.png"},{"id":92569957,"identity":"574d11a8-c498-419b-b345-32d657b73a1c","added_by":"auto","created_at":"2025-10-01 07:31:46","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":747476,"visible":true,"origin":"","legend":"\u003cp\u003eOverview of analyst-defined spectral grouping classification for Group 5, comparing data from the KBr/LN-MCT-B (top) and the ZnSe/TE-MCT-A (bottom), showing subtle visual variation trends in spectra between the two instruments. KKT has been applied to these spectra.\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-7711776/v1/aaca36e6c08242946e031662.png"},{"id":92569993,"identity":"f9b25ddd-25ae-4577-8b74-0bda34635667","added_by":"auto","created_at":"2025-10-01 07:31:49","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":417102,"visible":true,"origin":"","legend":"\u003cp\u003eOverview of analyst-defined spectral grouping classification for Group 6, comparing data from the KBr/LN-MCT-B (top) and the ZnSe/TE-MCT-A (bottom), showing subtle visual variation trends in spectra between the two instruments. KKT has been applied to these spectra.\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-7711776/v1/2bd49e9b94474d28f79d8c0e.png"},{"id":92570800,"identity":"bfeed1d4-c270-4d98-bd1d-258b5e6a1d36","added_by":"auto","created_at":"2025-10-01 07:39:47","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":487794,"visible":true,"origin":"","legend":"\u003cp\u003ePCA scores plots of the ZnSe/TE-MCT-A and KBr/LN-MCT-B KKT-truncated and preprocessed datasets, visualized by adhesive composition (left) and analyst-defined groupings (right). The ZnSe/TE-MCT-A dataset (top row) was processed with first derivative, SNV, and GLSW corrections, while the KBr/LN-MCT-B dataset (bottom row) was processed with SNV, GLSW, and mean centering.\u003c/p\u003e","description":"","filename":"10.png","url":"https://assets-eu.researchsquare.com/files/rs-7711776/v1/51c72ee65f04493333dbd0c3.png"},{"id":92570000,"identity":"d681ce10-d74d-4b6e-9fe7-0d0c8f8237bc","added_by":"auto","created_at":"2025-10-01 07:31:49","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":330812,"visible":true,"origin":"","legend":"\u003cp\u003ePCA scores plots of the ZnSe/TE-MCT-A and KBr/LN-MCT-B raw-truncated and preprocessed datasets, visualized by adhesive composition. The ZnSe/TE-MCT-A dataset (left) was preprocessed using first derivative, SNV, and GLSW corrections, while the KBr/LN-MCT-B dataset (right) was processed with SNV, GLSW, and mean centering.\u003c/p\u003e","description":"","filename":"11.png","url":"https://assets-eu.researchsquare.com/files/rs-7711776/v1/fc651447e763fcb39541db2c.png"},{"id":92570807,"identity":"87e99881-e589-44ba-ba04-f65173cbfe50","added_by":"auto","created_at":"2025-10-01 07:39:49","extension":"jpg","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":18227,"visible":true,"origin":"","legend":"\u003cp\u003ePCA scores plot of the KBr/LN-MCT-B dataset (extended spectral range).\u003c/p\u003e","description":"","filename":"12.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7711776/v1/1f7b5b84f291d5cd8b472d5e.jpg"},{"id":92569973,"identity":"6a1817ac-7a89-4427-862d-6d5c6b225890","added_by":"auto","created_at":"2025-10-01 07:31:47","extension":"png","order_by":13,"title":"Figure 13","display":"","copyAsset":false,"role":"figure","size":44290,"visible":true,"origin":"","legend":"\u003cp\u003eHCA dendrogram of the ZnSe/TE-MCT-A KKT-truncated and smoothed dataset, processed with first derivative, SNV, and GLSW corrections. The analysis was performed on a single representative spectrum per sample to visualize clustering patterns based on adhesive composition.\u003c/p\u003e","description":"","filename":"13.png","url":"https://assets-eu.researchsquare.com/files/rs-7711776/v1/4ce98eddbaba207868680f69.png"},{"id":92569988,"identity":"4d15a4d1-f98a-484f-a50f-0814083f53d2","added_by":"auto","created_at":"2025-10-01 07:31:48","extension":"jpg","order_by":14,"title":"Figure 14","display":"","copyAsset":false,"role":"figure","size":55461,"visible":true,"origin":"","legend":"\u003cp\u003ePCA scores plot of validation set projected onto the model of ZnSe/TE-MCT-A raw data after preprocessing with first derivative, SNV, and GLSW corrections.\u003c/p\u003e","description":"","filename":"14.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7711776/v1/dcdb7c9f2cfb000e78cd7fcc.jpg"},{"id":92569963,"identity":"91c1154f-828a-4139-acda-20e8b992b5e1","added_by":"auto","created_at":"2025-10-01 07:31:47","extension":"jpg","order_by":15,"title":"Figure 15","display":"","copyAsset":false,"role":"figure","size":25374,"visible":true,"origin":"","legend":"\u003cp\u003ePCA scores plot of orientation-shifted spectra projected onto the model of ZnSe/TE-MCT-A raw data after preprocessing with first derivative, SNV, and GLSW corrections.\u003c/p\u003e","description":"","filename":"15.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7711776/v1/5c071156e81c5d8921041a8b.jpg"},{"id":92569965,"identity":"d2b7d7c7-7cc0-4bb6-8a0a-000b2c87aad5","added_by":"auto","created_at":"2025-10-01 07:31:47","extension":"png","order_by":16,"title":"Figure 16","display":"","copyAsset":false,"role":"figure","size":401970,"visible":true,"origin":"","legend":"\u003cp\u003eComparative FTIR spectra of selected Exp. 91 samples against reference materials. On top, sample 91-17 (bone glue) provides the strongest match to its reference counterpart (INFRA-ART), illustrating clear spectral correspondence and the potential of FTIR for identifying proteinaceous adhesives. In contrast, shown below, sample 91-23 (pine resin and fat mixture) shows a poor match to modern pine tar (Monnier), underscoring the challenges of interpreting composites and the potential divergence between composite mixtures and available reference spectra. Additional comparisons are provided in the Supplementary Information (SF 1-4) for reference.\u003c/p\u003e","description":"","filename":"14.png","url":"https://assets-eu.researchsquare.com/files/rs-7711776/v1/77a163ac786fce0eca32f803.png"},{"id":92677675,"identity":"b0298263-8371-4551-8b16-22d0aaf899cc","added_by":"auto","created_at":"2025-10-02 23:16:24","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":7973431,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7711776/v1/b53821fa-249c-481c-b3fb-82017f0ad7c6.pdf"},{"id":92569997,"identity":"9ad5e5c6-58a0-4af2-a4b0-86ba3a8d4e9a","added_by":"auto","created_at":"2025-10-01 07:31:49","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":26423984,"visible":true,"origin":"","legend":"","description":"","filename":"Lienetal.2025SupplementaryInformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-7711776/v1/24062be698fc635cf5839f62.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"FTIR Analysis of Experimental Adhesives: Investigating Spectral Reproducibility, Chemometric Approaches, and Archaeological Applications","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe microscopic study of residues on lithic artifacts plays a critical role in archaeological reconstructions of ancient lifeways, offering rare and direct evidence of tool function, resource selection and use, technological behaviors, and complex cognitive processing skills (Williamson \u003cspan citationid=\"CR155\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Mazza et al. \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Wadley \u003cspan citationid=\"CR141\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Wragg Sykes \u003cspan citationid=\"CR157\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Rots et al. \u003cspan citationid=\"CR116\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Zupancich et al. \u003cspan citationid=\"CR159\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Degano et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Venditti et al. \u003cspan citationid=\"CR139\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Kozowyk et al. \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Among the most informative of these residues are adhesives\u0026mdash;particularly compound adhesives, those which consist of multiple ingredients\u0026mdash;used in the hafting of composite tools (Aveling and Heron \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; Rots \u003cspan citationid=\"CR112\" class=\"CitationRef\"\u003e2008a\u003c/span\u003e; Wadley et al. \u003cspan citationid=\"CR143\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Wadley \u003cspan citationid=\"CR141\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Zipkin et al. \u003cspan citationid=\"CR158\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Rageot et al. \u003cspan citationid=\"CR106\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Chasan et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Such materials hold immense potential for understanding complex technological processes and the cognitive capacities of past populations (Wadley et al. \u003cspan citationid=\"CR143\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Wadley \u003cspan citationid=\"CR141\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Wragg Sykes \u003cspan citationid=\"CR157\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Rots et al. \u003cspan citationid=\"CR117\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). However, identifying and interpreting such residues can be fraught with methodological challenges (Wadley and Lombard 2007; Rots \u003cspan citationid=\"CR113\" class=\"CitationRef\"\u003e2008b\u003c/span\u003e; Prinsloo et al. \u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Rots et al. \u003cspan citationid=\"CR116\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Nucara et al. \u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Cnuts and Rots \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Issues of preservation (Langejans \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Zurro and Gadekar \u003cspan citationid=\"CR160\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), contamination (Croft et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Pedergnana \u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Cnuts et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Frahm et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Zurro and Gadekar \u003cspan citationid=\"CR160\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), and interpretive ambiguity (Crowther and Haslam 2007; Wadley and Lombard 2007; Pedergnana \u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Zurro and Gadekar \u003cspan citationid=\"CR160\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) continue to limit the field\u0026rsquo;s ability to derive robust conclusions from the presence of residues alone.\u003c/p\u003e\u003cp\u003eWithin this context, Fourier transform infrared (FTIR) spectroscopy has become increasingly recognized as a valuable, non-destructive analytical technique capable of characterizing organic and inorganic compounds at a microscopic scale, with broad application across many sub-fields of archaeological science (e.g. Shillito et al. \u003cspan citationid=\"CR126\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Weiner \u003cspan citationid=\"CR145\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Nunziante-Cesaro and Lemorini \u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Solodenko et al. \u003cspan citationid=\"CR131\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Bradtmoller et al. 2016; Monnier et al. \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2017a\u003c/span\u003e; Monnier et al. \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e2017b\u003c/span\u003e; Monnier \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Lemorini et al. \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Dominici et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Mentzer \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Beginning in the mid-20th century and gaining steady traction since, FTIR has been a versatile tool in related disciplines. Adopted into geological studies in the 1950s, FTIR has enabled the identification and characterization of minerals and proved itself as a reliable method alongside traditional petrographic approaches (Keller and Pickett \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e1950\u003c/span\u003e; Launer \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e1952\u003c/span\u003e; Adler and Kerr, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e1965\u003c/span\u003e; Farmer \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e1974\u003c/span\u003e). Applications of FTIR to the study of sediments in archaeological contexts were pioneered largely by Steve Weiner, who began using the technique to explore the relationship between bone preservation, secondary minderals and archaeological site formation processes (DeNiro and Weiner \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e1988\u003c/span\u003e; Weiner and Goldberg \u003cspan citationid=\"CR146\" class=\"CitationRef\"\u003e1990\u003c/span\u003e; Weiner et al. \u003cspan citationid=\"CR148\" class=\"CitationRef\"\u003e1993\u003c/span\u003e; Weiner et al. \u003cspan citationid=\"CR147\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). Other strong applications include sourcing and characterization of amber and chert in provenance studies and paleoenvironmental reconstruction (Galletti and Mazzeo 1993; Angelini and Bellintani \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Wolfe et al. \u003cspan citationid=\"CR156\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Parish et al. \u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Qu et al. \u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Havelcova et al. 2016). Reflectance FTIR analyses, which can be conducted in a fully non-destructive manner depending on sample configuration, have also been applied to address similar research questions (e.g. mineral identification and site formation processes: Berna \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2017\u003c/span\u003e, Morrissey et al. \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; and chert sourcing: Parish et al. \u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e2013\u003c/span\u003e, Parish \u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e2016\u003c/span\u003e, Sch\u0026uuml;rch et al. \u003cspan citationid=\"CR123\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eMore recently, FTIR has shown considerable promise in identifying a wide range of organic archaeological residues, including plant gums, resins, waxes, and proteinaceous materials (Cinta-Pinzaru et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Nunziante-Cesaro and Lemorini \u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Bruni and Guglielmi \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Martin-Ramos et al. \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Lemorini et al. \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Aleo et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In particular, its use in reflectance mode is non-destructive, which allows for minimal sample preparation and the preservation of specimens for future analyses (Tappert et al. \u003cspan citationid=\"CR132\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Beasley et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Prinsloo et al. \u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Monnier \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Lemorini et al. \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Despite these advantages, FTIR is still underutilized for residue analysis in archaeological contexts, in part because of a lack of methodological standardization (Nunziante-Cesaro and Lemorini \u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Monnier \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), gaps in the coverage of reference libraries (Prinsloo et al. \u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), and difficulties distinguishing residue signals from those of the underlying substrates or from other sources of interference such as post-depositional transformation and contamination (Monnier \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; McAdams et al. \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Frahm et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Without careful methodological controls, these signals can easily be misinterpreted, potentially skewing interpretations of tool use and behavior (Lopez-Ballester et al. \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Vahur et al. \u003cspan citationid=\"CR137\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Cnuts et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Frahm et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThis paper builds on previous research exploring the application of FTIR to lithic residue analysis, with a specific focus on the reliability of detection and classification of adhesive residues (Boeda et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; Bradtmoller et al. 2016; Kozowyk et al. \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Despotopoulou et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). While destructive techniques including Gas Chromatography-Mass Spectrometry (GC-MS) have been highly effective for identifying specific chemical compounds related to adhesives (Degano et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Deviese et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Chen et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Chasan et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), their invasive nature makes them less desirable for early-stage screening or analysis of limited archaeological materials (Prinsloo et al. \u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Degano et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). However, the reliability of FTIR data is highly dependent on the development of robust protocols, experimental validation, analyst skill, and clearly defined spectral libraries (Weiner \u003cspan citationid=\"CR145\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Bruni and Guglielmi \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Lettieri \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Monnier et al. \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2017a\u003c/span\u003e, \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e2017b\u003c/span\u003e; Monnier \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Cortea et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). While reference libraries are essential for interpreting organic residues, they are inherently limited when applied to archaeological materials, which often exhibit complex and site-specific diagenetic transformations (Langejans \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Monnier \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; McAdams et al. \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). These transformations can alter the chemical structure and spectral signatures of organic residues in ways that are not easily predicted or replicated in controlled experimental conditions. As a result, it is unlikely that comprehensive reference libraries encompassing all possible post-depositional scenarios will ever be developed.\u003c/p\u003e\u003cp\u003eThe role of inter-instrument variability remains a consideration for spectral reproducibility as well. Recent studies by Pothier-Bouchard et al. (2019) and Quiles et al. (\u003cspan citationid=\"CR105\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) have applied this concept, testing various instruments in both portable and benchtop formats, to assess collagen preservation in zooarchaeological and human bone samples. The results demonstrated that spectral data are not fully interchangeable between instruments. Discrepancies were attributed to differences in spectral resolution, signal-to-noise ratio, and analytical configuration (Pothier-Bouchard et al. 2019; Quiles et al. \u003cspan citationid=\"CR105\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). These findings indicate that different instruments introduce variability in spectra, with possible additional factors of influence including compositional variation in source material and unstandardized post-depositional transformation of samples. In cases related to adhesive studies, minor changes in recipe or adhesive processing steps may have a profound impact on the success of spectral reproducibility. Likewise, intra-instrument \u0026ndash; and even intra-analyst \u0026ndash; spectral variability is a viable concern, particularly for the application of reflectance mode spectral collection on samples with fluctuations in surface topography and matrix structure.\u003c/p\u003e\u003cp\u003eChemometrics, when applied to FTIR, has the goal of extracting chemical information from spectra with or without exact chemical identification. The approach typically utilizes multivariate statistical modeling to handle the large number of variables inherent to spectral datasets and aid in classification or quantification. Principal component analysis (PCA) offers a valuable multivariate tool for reducing the complexity of spectroscopic datasets and visualizing underlying patterns of variance that may correspond to compositional differences (De Benedetto et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Baxter \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Chatterjee et al. 2018; Pi\u0026ntilde;a-Torres et al. \u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Wertz et al. \u003cspan citationid=\"CR149\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In the context of FTIR residue studies, where spectra can be influenced by a combination of material chemistry, substrate interference, and instrumental variability, PCA can aid in extracting dominant trends while minimizing the confounding effects of spectral noise and baseline variation (Medeghini et al. 2015; Pi\u0026ntilde;a-Torres et al. \u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Wertz et al. \u003cspan citationid=\"CR149\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). By transforming spectral data into principal components that capture the most significant sources of variance, PCA provides a data-driven means of assessing whether adhesives with distinct chemical formulations naturally cluster in multivariate space (Pi\u0026ntilde;a-Torres et al. \u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). This can complement visual and library-based identifications by revealing relationships not immediately evident in spectral data, especially when dealing with complex or composite residues.\u003c/p\u003e\u003cp\u003eTo address known challenges, this study employs an experimental approach. First, we present the comparative results of FTIR analyses conducted on a suite of experimentally produced adhesives, chosen to replicate common substances utilized in prehistoric hafting practices. These samples were analyzed using two different FTIR microscopes across an eight-year interval, allowing us to assess instrument variability and reproducibility of spectral results. To a lesser extent, intra-instrument variability due to sample positioning was also tested. Next, by applying both analyst specific visual classification and statistical clustering techniques via principal component analysis (PCA) and hierarchical cluster analysis (HCA), we explore how compositional and contextual factors influence spectral quality and interpretability and the robusticity of applying statistical methods to group materials of unknown composition. In this workflow, we see grouping of samples as a necessary prerequisite to the eventual identification of individual adhesive components.\u003c/p\u003e\u003cp\u003eAdditionally, we briefly discuss the effects of substrate interference, especially when adhesives are transparent or present in thin layers or small deposits. A significant challenge in FTIR-based residue analysis lies in distinguishing between the spectra of the adhesive and those of the underlying lithic material (Coats \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Nunziante-Cesaro and Lemorini, \u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Beasley et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Glavcheva et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). This is especially true when the substrate, including chert or quartz, produces strong reflectance signals that may dominate or obscure the adhesive\u0026rsquo;s own signatures. During this analysis, transparent substances applied to flint substrates illustrate how factors including residue thickness and surface reflectivity impact the diagnostic quality of the spectra.\u003c/p\u003e\u003cp\u003eThis study emphasizes the importance of experimental approaches in validating and refining residue analysis methods in archaeology, highlighting key methodological variables that must be accounted for such as the influence of storage conditions, machine variability, substrate interference, diagenetic effects, and analyst subjectivity (Langejans \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Nucara et al. \u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Dominici et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Frahm et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). By generating new reference spectra of composite adhesives and evaluating instrument-to-instrument accuracy as well as the applicability of FTIR and PCA/HCA for adhesive residue classification, we aim to contribute to the development of a more standardized and reliable framework for residue analysis on lithic tools. The real strength of the present approach lies in providing a methodological framework for navigating the interpretive uncertainty associated with degraded or chemically altered archaeological materials (De Benedetto et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Gallello et al. 2013). By applying multivariate techniques such as PCA and HCA, it becomes possible to identify spectral patterns based on compositional similarity of a defined collection, regardless of whether those residues match pristine modern references. This strategy enables analysts to detect meaningful groupings of adhesives within a single site or across related assemblages, even when the exact identity of the materials cannot be confidently determined. In doing so, it offers a scalable and reproducible tool for exploring technological variation in ancient adhesive use, despite the interpretive limitations imposed by diagenesis.\u003c/p\u003e\u003cp\u003eUltimately, the accurate identification or classification of residues, particularly adhesive compounds, has far-reaching implications for understanding prehistoric technologies (Mazza et al. \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Wadley \u003cspan citationid=\"CR141\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Zipkin et al. \u003cspan citationid=\"CR158\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Schmidt et al. \u003cspan citationid=\"CR122\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Adhesive production often requires complex multi-step processes, involving informed resource selection, knowledge of material properties, skilled manipulation, and controlled application of heat or additive treatments (Regert et al. \u003cspan citationid=\"CR109\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Wadley et al. \u003cspan citationid=\"CR143\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Rageot et al. \u003cspan citationid=\"CR106\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Chasan et al. 2021). These processes are indicative not only of technological sophistication but also of advanced cognitive capacities, including planning depth, working memory, and an understanding of cause-and-effect relationships (Wadley \u003cspan citationid=\"CR141\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Wilkins et al. 2012; Wragg Sykes \u003cspan citationid=\"CR157\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). As such, every methodological advancement in classifying or identifying, and interpreting these residues deepens our ability to reconstruct the technological and cognitive landscapes of ancient human populations.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Materials\u003c/h2\u003e\u003cp\u003eMaterials analyzed in this study consist of experimental adhesive samples deposited onto flint flakes prepared by an experienced primitive technologist (Christian Lepers, TraceoLab, Li\u0026egrave;ge, Belgium). All samples were unaltered by post-depositional processes or experimental degradation, stored over the 8-year span in individual plastic bags in a temperature stable and light-safe location. Though not directly comparable to archaeological specimens, they serve as a useful basis for examining spectral behavior and assessing stability in peak presence within the individual substances. The methodological complications observed in this study would likely be amplified in archaeological contexts due to greater material degradation overall, degradation initiated by removal from the substrate, possible contamination, and other environmental factors.\u003c/p\u003e\u003cp\u003eSamples consisted of base ingredients with different additives to represent various types of natural adhesive materials commonly available within prehistoric contexts, plus other more modern representations of natural glues. Base ingredient types included pine and spruce resins (\u003cem\u003ePinus nigra\u003c/em\u003e and \u003cem\u003ePicea abies\u003c/em\u003e), acacia gum, birch tar, sinew glue, bone glue, and hide glue. Additives included beeswax (up to 50%), charcoal, flax, ochre, beef fat (10\u0026ndash;20%), sand (10\u0026ndash;20%), and clay (10\u0026ndash;20%) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). All components were measured by weight in grams.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eComposition of experimental adhesive samples with corresponding additive proportions and group assignments. Each sample is listed with its primary adhesive, as well as proportions of the primary adhesive, beeswax, and other additives (OA), along with the groups designated by analysts 1 and 2 during analyst-defined visual classifications used in the study. Adhesive recipes were either drawn directly from or inspired by the corresponding references.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"9\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSample ID\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePrimary Ingredient\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eResin/Glue %\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eBeeswax %\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eOther Additives (OA)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eOA %\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eGroup (1st)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eGroup (2nd )\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e91\u0026thinsp;\u0026minus;\u0026thinsp;01 /\u003c/p\u003e\u003cp\u003e91\u0026thinsp;\u0026minus;\u0026thinsp;35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePinus nigra corsicana\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e100%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eDegano et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2019\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e91\u0026thinsp;\u0026minus;\u0026thinsp;02 /\u003c/p\u003e\u003cp\u003e91\u0026thinsp;\u0026minus;\u0026thinsp;36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePinus nigra corsicana\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e50%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e50%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eDegano et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; 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Degano et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2019\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e91\u0026thinsp;\u0026minus;\u0026thinsp;04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePicea abies\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e50%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e50%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eHelwig et al. \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Degano et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2019\u003c/span\u003e;\u003c/p\u003e\u003cp\u003eSano et al. \u003cspan citationid=\"CR118\" class=\"CitationRef\"\u003e2019\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e91\u0026thinsp;\u0026minus;\u0026thinsp;05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAcacia gum\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e100%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eWadley \u003cspan citationid=\"CR142\" class=\"CitationRef\"\u003e2005\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e91\u0026thinsp;\u0026minus;\u0026thinsp;06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAcacia gum\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e50%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e50%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eWadley \u003cspan citationid=\"CR142\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Sano et al. \u003cspan citationid=\"CR118\" class=\"CitationRef\"\u003e2019\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e91\u0026thinsp;\u0026minus;\u0026thinsp;07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBirch tar\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e100%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eRegert et al. \u003cspan citationid=\"CR108\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; Mazza et al. \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Niekus et al. \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e2019\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e91\u0026thinsp;\u0026minus;\u0026thinsp;08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBeeswax\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e100%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eBaales et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2017\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e91\u0026thinsp;\u0026minus;\u0026thinsp;09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePinus nigra corsicana\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e80%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eCharcoal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e20%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eRots et al. \u003cspan citationid=\"CR115\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Bradtmoller et al. 2016; Degano et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2019\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e91\u0026thinsp;\u0026minus;\u0026thinsp;10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePinus nigra corsicana\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e40%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e40%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eCharcoal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e20%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eRots et al. \u003cspan citationid=\"CR115\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Bradtmoller et al. 2016; Degano et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2019\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e91\u0026thinsp;\u0026minus;\u0026thinsp;11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePinus nigra corsicana\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e80%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eFlax\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e20%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eWadley \u003cspan citationid=\"CR142\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; 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Bradtmoller et al. 2016; Degano et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2019\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e91\u0026thinsp;\u0026minus;\u0026thinsp;14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePinus nigra corsicana\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e40%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e40%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eOchre\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e20%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eRots et al. \u003cspan citationid=\"CR115\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Bradtmoller et al. 2016; Degano et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2019\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e91\u0026thinsp;\u0026minus;\u0026thinsp;15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSinew glue (commercial)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e100%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eCnuts et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Tydgadt and Rots 2022\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e91\u0026thinsp;\u0026minus;\u0026thinsp;16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSinew glue (experimental)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e100%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eCnuts et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Tydgadt and Rots 2022\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e91\u0026thinsp;\u0026minus;\u0026thinsp;17 / 91\u0026thinsp;\u0026minus;\u0026thinsp;33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBone glue (commercial)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e100%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eTydgadt and Rots 2022\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e91\u0026thinsp;\u0026minus;\u0026thinsp;19 / 91\u0026thinsp;\u0026minus;\u0026thinsp;34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDeer hide glue (experimental)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e100%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eTydgadt and Rots 2022\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e91\u0026thinsp;\u0026minus;\u0026thinsp;20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSheep hide glue (experimental)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e100%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eTydgadt and Rots 2022\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e91\u0026thinsp;\u0026minus;\u0026thinsp;21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePicea abies\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e70%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e30%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eHelwig et al. \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Degano et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2019\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e91\u0026thinsp;\u0026minus;\u0026thinsp;22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePicea abies\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e90%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eBeef fat\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e10%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eRegert et al. \u003cspan citationid=\"CR108\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; Helwig et al. \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Degano et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2019\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e91\u0026thinsp;\u0026minus;\u0026thinsp;23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePinus nigra corsicana\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e90%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eBeef fat\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e10%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eRegert et al. \u003cspan citationid=\"CR108\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; Degano et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2019\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e91\u0026thinsp;\u0026minus;\u0026thinsp;24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePicea abies\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e90%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eBeef fat - overheated\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e10%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eRegert et al. \u003cspan citationid=\"CR108\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; Helwig et al. \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Cnuts et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Degano et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2019\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e91\u0026thinsp;\u0026minus;\u0026thinsp;25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePicea abies\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e90%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eBeef fat \u0026ndash; frequently heated\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e10%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eRegert et al. \u003cspan citationid=\"CR108\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; Helwig et al. \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Cnuts et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Degano et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2019\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e91\u0026thinsp;\u0026minus;\u0026thinsp;26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePicea abies\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e80%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eOchre\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e20%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eRots et al. \u003cspan citationid=\"CR115\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Helwig et al. \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Bradtmoller et al. 2016; Degano et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2019\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e91\u0026thinsp;\u0026minus;\u0026thinsp;27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePicea abies\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e80%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eOchre\u0026thinsp;+\u0026thinsp;Sand\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e10% + 10%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eBradtmoller et al. 2016; Degano et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2019\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e91\u0026thinsp;\u0026minus;\u0026thinsp;28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePicea abies\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e80%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eOchre\u0026thinsp;+\u0026thinsp;Clay\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e10% + 10%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eHelwig et al. \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Degano et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2019\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e91\u0026thinsp;\u0026minus;\u0026thinsp;29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePicea abies\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e80%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSand\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e20%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eHelwig et al. \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; 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Degano et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2019\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e91\u0026thinsp;\u0026minus;\u0026thinsp;31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePicea abies\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e80%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eBeef fat\u0026thinsp;+\u0026thinsp;Sand\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e10% + 10%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eRegert et al. \u003cspan citationid=\"CR108\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; Helwig et al. \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Degano et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2019\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e91\u0026thinsp;\u0026minus;\u0026thinsp;32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePicea abies\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e80%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eBeef fat\u0026thinsp;+\u0026thinsp;Clay\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e10% + 10%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eRegert et al. \u003cspan citationid=\"CR108\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; Helwig et al. \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Degano et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2019\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Methods\u003c/h2\u003e\u003cp\u003eThe initial round of FTIR analysis took place in 2017\u0026ndash;2018 at the University of T\u0026uuml;bingen, where approximately 140 spectra were generated with additional scans collected as needed. These spectra were obtained using an Agilent 610 FTIR microscope equipped with a potassium bromide (KBr) beamsplitter and a wide band, liquid nitrogen cooled mercury-cadmium-telluride (LN-MCT-B) detector operated in reflectance mode. The spectral range of FTIR instruments is determined by a number of different factors, including the source, beamsplitters and detectors; this particular combination yields a spectral range of 4000\u0026thinsp;\u0026minus;\u0026thinsp;400 cm⁻\u0026sup1; in reflectance mode, of which 4000\u0026thinsp;\u0026minus;\u0026thinsp;450 cm⁻\u0026sup1; is generally usable. The microscope was coupled to an Agilent 660 bench FTIR instrument with ceramic source, running the Resolutions Pro software. Background scans were collected on gold, and the spectral resolution was set at 2 cm⁻\u0026sup1;. The exact number of co-added scans varied between 180\u0026ndash;200 per spectrum. For each sample, spectra were typically collected from the adhesive deposit using the largest analytical area available (approximately 600 x 600 \u0026micro;m) in order to reduce spectral noise. In cases where inclusions were present, spectra were also collected from overlapping areas with reduced aperture; however, due to poor spectral quality, these are largely excluded from the present study. Examples of sampling conditions are illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003eThe second round of data collection took place in 2024\u0026ndash;2025 at the TraceoLab (University of Li\u0026egrave;ge) and was carried out using a Bruker LUMOS II FTIR microscope, also in reflectance mode. This instrument was equipped with an internal ceramic source, a zinc selenide (ZnSe) beamsplitter and a thermoelectrically cooled mercury-cadmium-telluride (TE-MCT) detector with a spectral range of 4000\u0026thinsp;\u0026minus;\u0026thinsp;650 cm⁻\u0026sup1;, operating through the Bruker OPUS software. As in the earlier analysis, background scans were collected on gold, and spectral resolution was maintained at 2 cm⁻\u0026sup1;. Scan parameters were standardized at 400 co-added scans per spectrum, with a uniform aperture of 400 \u0026times; 400 \u0026micro;m for all measurements.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe instrumentation and sampling parameters are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and the two systems are hereafter referred to as KBr/LN-MCT-B and ZnSe/TE-MCT-A. It should be noted that these two systems represent two very common configurations of optical components for laboratory-grade instruments, with several different manufacturers producing similar bench-coupled and stand-alone FTIR microscopes. TE-MCT detectors are typically available on newer models. Wide-band versions (designated as MCT-B) have broader spectral range, but lower sensitivity compared to MCT-A (sometimes called \u0026ldquo;high-sensitivity\u0026rdquo;). Samples were minimally handled and mounted on glass slides or molded platforms covered with parafilm during analysis. For both the initial and second round of spectra collection, each sample underwent scans at a minimum of three points, and in some cases up to eight, to ensure representational spectral quality across the sample area and to account for the potential variability caused by inclusions. Due to the level of topography within the analytical areas, attention was also paid to the field of view focus, maximizing the area that would yield a good signal. Despite automated mapping being available on both systems and in similar systems available from other manufacturers, all analyses were conducted manually due to the need to significantly adjust the focus between points. During the 2024/2025 analysis, four of the original samples could not be reanalyzed and thus were reproduced to the same specifications. One piece that was originally included, 91\u0026thinsp;\u0026minus;\u0026thinsp;18, has been excluded from this study because it could not be replicated for the second component of analysis.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eFTIR instrument configurations used in this study.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInstrument\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIR Source\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eBeamsplitter\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDetector\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSpectral range\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eResolution applied\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eAperture applied\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAgilent Cary 660/610\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCeramic (external via the 660)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePotassium-bromide (KBr)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eLiquid nitrogen cooled Mercury-cadmium-telluride type B (LN-MCT-B)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4000-400cm⁻\u0026sup1;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2cm⁻\u0026sup1;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e600 x 600 \u0026micro;m\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBruker LUMOS II\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCeramic (internal)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eZinc-selenide (ZnSe)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eThermoelectrically cooled Mercury-cadmium-telluride type A (TE-MCT)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4000-650cm⁻\u0026sup1;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2cm⁻\u0026sup1;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e400 x 400 \u0026micro;m\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003ePrior to visual assessment by the analysts, all spectra were processed by removing carbon dioxide peaks to reduce atmospheric interference, and a Kramers-Kronig transformation (KKT) was applied to convert reflectance spectra into a form that more closely approximates transmission spectra. The application of KKT fundamentally alters the shape of reflectance spectra by correcting for dispersive distortions inherent in reflectance measurements, with such distortions often producing features reminiscent of spectral derivatives (e.g. a signal below the baseline just before a strong peak), strong inverted bands (reststrahlen bands), baseline shifts, and adjustments in peak intensity and position that may alter interpretation (Vetter and Schreiner \u003cspan citationid=\"CR140\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Miliani et al. \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Monico et al. \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Prinsloo et al. \u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Invernizzi et al. \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Spectra were then categorized into high, medium, or low quality based on the presence of visible peaks and their signal-to-noise ratios. These steps were carried out using Resolutions Pro and OPUS software, respectively. Any sample that yielded only one high-quality spectrum was re-analyzed to obtain additional data for more robust classification.\u003c/p\u003e\u003cp\u003eTo assess the spectral similarity between instruments and evaluate reproducibility, RStudio version 4.4.3 equipped with tidyverse (Wickham et al. \u003cspan citationid=\"CR154\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), readxl (Wickham and Bryan \u003cspan citationid=\"CR152\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), pracma (Borchers \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), stringr (Wickham \u003cspan citationid=\"CR151\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), ggplot2 (Wickham \u003cspan citationid=\"CR150\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), FactoMineR (Le et al. \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), factoextra (Kassambra and Mundt \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), and missMDA (Josse and Husson \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) packages was used to apply several statistical methods to paired spectra across three conditions: (1) raw data, (2) KKT-transformed data without intensity normalization, and (3) KKT-transformed data with standard normalization of a maximum %Reflectance y-value of 0.5 applied. These applied statistics included average difference and standard deviation, root mean square error (RMSE), coefficient variance (CV), cosine similarity, Pearson\u0026rsquo;s correlation coefficient (PCC) Wilcoxon signed-rank test (p), and Cohen\u0026rsquo;s d. Due to differences in spectral range between the two machines, spectra from the KBr/LN-MCT-B were truncated to 4000\u0026thinsp;\u0026minus;\u0026thinsp;650 cm⁻\u0026sup1; to align with the range of the ZnSe/TE-MCT-A-generated spectra for both comparative testing. To preserve as many original wavenumbers as possible, no smoothing was performed. While this precise combination of statistical methods (RMSE, CV, cosine similarity, Wilcoxon test, and Cohen\u0026rsquo;s d) in the context of paired FTIR spectra has not, to our knowledge, been directly described in existing spectroscopy literature, each metric is firmly rooted in established analytical and chemometric practice. Cosine similarity is widely used to quantify spectral shape agreement in areas such as near-infrared analysis and mass-spectrometry (Bittremieux et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Guo et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). RMSE and coefficient of variation are standard figures of merit for evaluating quantitative model performance in chemometrics (Sila et al. \u003cspan citationid=\"CR129\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Ghosh et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Guo et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Furthermore, reproducibility and stability assessments in mass and Raman spectroscopy frequently utilize correlation-based measures, intensity variation, clustering, and spectral similarity metrics (Guo et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Therefore, our comprehensive application of these complementary metrics provides a rigorously grounded, multidimensional framework for evaluating spectral similarity and reproducibility between instruments, anchored in the broader methodological landscape of analytical spectroscopy and chemometrics.\u003c/p\u003e\u003cp\u003eIntra-instrument variability and the effects of sample orientation on spectral reproducibility were also tested with a targeted subset of adhesive samples with known additives and visible inclusions. For each sample, multiple spectra were collected from the same point of analysis but with the sample reoriented on the FTIR platform. For each subsequent spectra, the sample was rotated by 90\u0026deg; in a counterclockwise direction. All scans were collected using the ZnSe/TE-MCT-A system, following the same parameters as described previously. This was done to simulate the inherent variability in archaeological analyses, where there is no standardized convention for how a lithic specimen should be oriented on the microscopic stage. In practice, orientation is often determined by the analyst\u0026rsquo;s judgement, guided by factors such as artifact morphology, surface accessibility, or stability on the stage; thus, artifact staging for spectral acquisition can vary widely across studies. Our tests therefore evaluated whether such orientation differences affect spectral shape, alter peak intensities, or introduce distortions linked to polarization effects or reflectance behavior associated with surface topography and anisotropy. The variability between orientation-shifted spectra was assessed with RStudio version 4.4.3 equipped with dplyr (Wickham et al. \u003cspan citationid=\"CR153\" class=\"CitationRef\"\u003e2025\u003c/span\u003ea), ggplot2 (Wickham \u003cspan citationid=\"CR150\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), tidyr (Wickham et al. \u003cspan citationid=\"CR153\" class=\"CitationRef\"\u003e2025\u003c/span\u003eb), stringr (Wickham \u003cspan citationid=\"CR151\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), effsize (Torchiano \u003cspan citationid=\"CR134\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), and broom (Robinson et al. \u003cspan citationid=\"CR110\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) packages. Statistical methods applied included calculations for standard deviation, coefficient of variation, Pearson correlation coefficient, cosine similarity, Cohen\u0026rsquo;s d, Wilcoxon signed-rank test (p), and Bland-Altman bias and limits of agreement. Within R, data was reshaped into wide format to enable pairwise comparison within each sample group, yielding six orientation pairs per sample, with calculations run for each pair.\u003c/p\u003e\u003cp\u003eFor the steps of classification and comparison, KKT-processed spectra without normalization and with CO2 peaks removed were evaluated using a reverse library search method within the Essential FTIR and OPUS software packages. All instrument specific spectra were compiled into a single reference library per machine and compared individually to assess similarity. Spectra from the KBr/LN-MCT-B were classified by a single analyst in T\u0026uuml;bingen, and spectra from the ZnSe/TE-MCT-A instrument were classified by a separate analyst in Li\u0026egrave;ge. Visual inspection by analysts was prioritized over automated rankings and library hit matches, as human evaluation often produced more accurate assessments of spectral similarity. To reflect a workflow that may be applied in archaeological settings, initial visual grouping of spectra was undertaken prior to identification efforts. This approach mirrors real-world scenarios in which ancient residues, having undergone diagenetic effects, likely will not match modern reference materials directly. In such cases, classification based on internal consistency across the assemblage can reveal shared chemical profiles, helping to identify sets of artifacts with similar residues even when external reference matches are unavailable. Thus, the analysts began by grouping spectra based on shared peak positions and intensities to establish internal structure within the datasets before pursuing material-specific identification.\u003c/p\u003e\u003cp\u003eInitial classification by analyst 1 grouped the spectra into nine categories, which were later refined into six distinct groups based on shared spectral characteristics. Initial classification by Analyst 2 grouped the spectra into eight categories, which were later refined into six with the knowledge that Analyst 1 had also arrived at six final groups, thereby facilitating direct comparison. Only peak positions and intensities were used for classification. At the time of analysis and initial grouping assignments, analysts were blind to sample composition as well as the results of the other analyst\u0026rsquo;s groupings, ensuring unbiased classification. The final consolidation into six aligned groups incorporated reference to analyst 1\u0026rsquo;s classification scheme, allowing the results presented here to be directly comparable. While external spectral libraries were consulted for later material identification, they played only a secondary role in identification due to interpretive challenges, with primary identification relying on visual comparison, direct peak matching, and spectral overlay. When performing a side-by-side comparison of spectra from each machine, the highest quality spectrum for each sample was manually selected based on signal-to-noise ratio and peak intensity. In the final stage of the visual classification, analyst 2 compared the six groups identified by each analysis and renumbered them, applying the same number when groups broadly matched. These groups are summarized in Table X under Group (1st ) and Group (2nd ). All images created for visual comparison between groups were produced in the Peak Spectroscopy software (Operant LLC).\u003c/p\u003e\u003cp\u003ePost-classification, external libraries were utilized to interpret the composition of materials to known standards. Libraries utilized include both reflectance spectra from the University of Minnesota Archaeological Materials Infrared Spectra Library and the Infrared and Raman Users Group (IRUG), and transmission spectra from the Helen and Martin Kimmel Center for Archaeological Science Infrared Spectra Library, IRUG, and the InfraArt Spectra Library (Price and Pretzel 2007; Monnier et al. \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2017a\u003c/span\u003e, \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e2017b\u003c/span\u003e; Cortea et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Weizmann Institute of Science). Comparison to the external reference libraries of spectra in transmission mode was performed visually to assess the reliability of KK-transformed reflectance spectra to align with those generated in transmission mode. All images created for visual comparison of samples with reference spectra were produced with Peak Spectroscopy.\u003c/p\u003e\u003cp\u003ePCA was applied to spectral data to explore patterns of variance within the collection in addition to analyst performed classification. All multivariate statistical analyses were conducted using PLS Toolbox version 9.5 (Eigenvector Research Inc.), which runs in a MATLAB R2024b (Mathworks) environment. To compare the datasets for chemometric modeling, a consistent preprocessing strategy was applied across both ZnSe/TE-MCT-A and KBr/LN-MCT-B data. Prior to preprocessing, spectra were truncated to the region between 1800\u0026ndash;650 cm⁻\u0026sup1; to isolate the fingerprint region and then smoothed with a 17-point Savitzky\u0026ndash;Golay (S-G) filter in Peak Spectroscopy to reduce noise while preserving peak shape. To assess the influence of the spectral range beyond the capabilities of the ZnSe/TE-MCT-A instrument on pattern detection, additional PCA was performed on KBr/LN-MCT-B datasets to the extended fingerprint zone from 1800\u0026thinsp;\u0026minus;\u0026thinsp;400 cm⁻\u0026sup1;, also treated with 17-point S-G smoothing. Extremely noisy or low-quality spectra as well as spectra from inclusions within the adhesive were excluded from the PCA to assess for true variance of the adhesive matrix. Multiple preprocessing strategies were tested before defining the most productive method. For the final PCA model reported here on KBr/LN-MCT-B datasets, to reduce multiplicative scatter and path-length variability, standard normal variate (SNV) preprocessing was applied as a normalization method, ensuring that the model would highlight relative variance rather than absolute signal intensity. In a similar application to Wertz et al. (\u003cspan citationid=\"CR149\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), generalized least squares weighting (GLSW) with a threshold of 0.05 was then applied to declutter the data, followed by mean centering. For ZnSe/TE-MCT-A models, the aforementioned preprocessing steps were similarly applied, however application of a 1st derivative (2nd polynomial) prior to SNV proved beneficial, and the application of mean centering had no influence on the results so this step was excluded (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Model robustness was assessed with Venetian-blind cross-validation with 10 folds and 3 samples per blind, with models run both with and without the cross-validation. These models were applied individually on raw and KKT treated data across both ZnSe/TE-MCT-A and KBr/LN-MCT-B systems. PCA was conducted also on two different grouping strategies, one based on analyst-defined classes developed through visual interpretation of spectral patterns without prior knowledge of adhesive composition, and another based on adhesive-defined classes reflecting the known chemical formulations of each sample defined by the predominant adhesive component (pinus-based, spruce-based, gum-based, protein-based, birch tar, and pure beeswax). The groups defined by predominant adhesive component are listed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, under the heading \u0026ldquo;Primary ingredient\u0026rdquo;. Beeswax was always considered a secondary component, even when present in equal amounts as the primary.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFollowing successful PCA, unsupervised cluster analysis was performed HCA in PLS Toolbox to evaluate whether natural groupings of adhesive types could be recovered from the data. HCA was conducted both directly on the preprocessed spectral data and on the PCA-transformed scores to compare the effectiveness of each approach. Running HCA on the spectral data alone preserved the full dimensionality and fine-grained spectral information, allowing an assessment of how well adhesives could be clustered without dimensionality reduction. However, HCA was also performed on the PCA scores, using the first three principal components, to reduce noise and emphasize the most informative variance while minimizing the influence of minor spectral fluctuations and measurement artifacts. The selection of three PCs was based on their cumulative explanation of the majority of structured variance within the dataset, ensuring a balance between retaining meaningful chemical distinctions and avoiding overfitting to noise-dominated components. HCA was performed using Ward\u0026rsquo;s method as the linkage criterion, which optimizes cluster formation by minimizing within-cluster variance, favoring compact and distinct groupings which present an advantage when dealing with overlapping or compositionally complex adhesive classes. Clustering was applied to the full datasets, as well as to reduced subsets that included either one representative spectrum per sample or a dataset with every other sample excluded, to assess the impact of replicate redundancy on clustering performance. This dual approach of testing HCA with and without PCA enabled a comprehensive evaluation of clustering robustness and clarified the benefits of dimensionality reduction in improving class resolution in spectroscopic datasets (Capobianco et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe selection of PCA and HCA in this study is grounded in their demonstrated utility for simplifying high-dimensional spectroscopic data and detecting chemically meaningful patterns. These methods are especially effective for datasets where overlapping signals, baseline shifts, and instrument noise complicate direct interpretation. While relatively underutilized in archaeological applications, PCA and HCA have proven successful in related domains, such as forensic residue analysis (Materazzi et al. \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Sharma and Sharma \u003cspan citationid=\"CR125\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), biomedical diagnostics (Wang and Mizaikoff \u003cspan citationid=\"CR144\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Rohman et al. \u003cspan citationid=\"CR111\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), and food science (Bendini et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Rahmania et al. 2015; Indrayanto and Rohman \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Hadaruga et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), where compound identification and material classification face similar spectral complexity. The use of such methods here reflects growing interest in applying chemometrics to archaeological FTIR data to enhance classification accuracy and reduce the subjectivity of visual interpretation. By pairing PCA with HCA, we increase model strength and interpretive confidence, particularly in assessing the internal consistency of adhesive groupings and evaluating the impact of preprocessing choices. This approach offers a methodological scaffold for future archaeological residue studies seeking to integrate multivariate tools, especially when diagenesis or the absence of direct reference materials limits traditional classification strategies.\u003c/p\u003e\u003cp\u003eTo test the robusticity and interpretive limits of the applied chemometric models, a blind validation set was included. This set consisted of ten samples, each composed of pure (100%) substance applied to a flint substrate, representing at least four of the six subsets (pinus-based, spruce-based, gum-based, birch tar, protein-based, and pure beeswax) from the primary adhesives within the main dataset. Unlike the composite mixtures used for model calibration, these validation samples were chemically simple, serving to evaluate how well the PCA model trained on complex, multi-component adhesives could handle single-component inputs of similar origin. The exact composition of each sample remained unknown to the analyst during scanning and analysis. Each sample was scanned two times under identical parameters using the ZnSe/TE-MCT-A system, generating 20 spectra in total. Following identical spectral processing parameters, the validation spectra were then projected onto the PCA models derived from the training datasets established with ZnSe/TE-MCT-A data. After projection, the distribution of validation points was evaluated relative to the PCA-defined clusters of the known training samples. Predicted class membership was inferred based on proximity in multivariate space. Once this was completed, the true adhesive composition of the validation samples was revealed and compared to the PCA-based predictions. This validation procedure was not designed exclusively as a conventional performance metric, but rather as a probe into the model\u0026rsquo;s behavior when confronted with datasets of fundamentally different structures as an attempt to test the scope and limitations of PCA as an exploratory tool for adhesive residue analysis. Because the PCA models were built on complex mixtures containing overlapping components, their ability to isolate and interpret pure inputs was expected to be somewhat limited. As such, the exercise served to highlight interpretive risks and provide insight into where and why the model may fail, information that is critical for understanding the broader applicability and limitations of PCA-based residue classification of adhesives.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e3.a. Instrument comparison and spectral processing\u003c/span\u003e\u003c/p\u003e\u003cp\u003eComparison of raw versus processed FTIR spectra revealed substantial improvements in cross-instrument alignment following application of the KKT, both with and without normalization. The mean Pearson correlation increased from \u0026minus;\u0026thinsp;0.256 in raw spectra to +\u0026thinsp;0.352 in both datasets post-processing, indicating a shift from inverse or negligible linear relationships to moderate positive agreement. Concurrently, the RMSE between spectra dropped sharply from 1.37 to 0.14 in KKT-not-normalized data and 0.18 in KKT-normalized data, underscoring the ability of the applied processing steps to reduce amplitude-related discrepancies. Effect size, measured by Cohen\u0026rsquo;s d, decreased from an extreme value of -5.69 in raw spectra to -0.78 in KKT-not-normalized and \u0026minus;\u0026thinsp;0.69 in KKT-normalized data, suggesting that instrument-based differences diminished considerably post-processing. Similarly, the standard deviation of differences shrank from 0.44 in raw to 0.09 in KKT-not-normalized and 0.13 in KKT-normalized data, reflecting greater consistency across spectra after processing. Interestingly, cosine similarity, which addresses vector shape independent of magnitude, dropped from 0.95 in raw spectra to 0.27 in both processed versions. This suggests that while KKT significantly improved alignment in amplitude and trend, it also introduced distortions in spectral shape, which presents a tradeoff that may affect analyses reliant on peak ratios or shape-based classification. Such processing may diminish performance in analyses reliant on relative peak structure, ratios, or chemometric classification, despite enhancing numerical comparability across instruments.\u003c/p\u003e\u003cp\u003eSpectra generated with the KBr/LN-MCT-B were in general fairly comparable to those generated with the ZnSe/TE-MCT-A, though there were often differences in peak height and occasional shifts in peak position, varying by up to 25 wavenumbers at most but on average falling within the expected range of variation between 2\u0026ndash;10 cm⁻\u0026sup1; (Hofko et al. \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Nemeth et al. \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Nicolau and Matzger \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; United States Pharmacopeia 2012). Peaks were usually skewed toward lower positions but in some cases also shifted higher. Most occurrences of shifted wavenumbers tend to occur within the OH and CO functional groups (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Aside from these differences, variation between the machines is primarily presented in spectral baseline, peak amplitude, and signal-to-noise ratio. Difference in signal-to-noise ratio is attributed primarily due to the increased number of co-added scans incorporated in the ZnSe/TE-MCT-A spectra. Despite these variances, spectral patterning was consistent between machines, aside from a few cases where the differences may be linked to inclusions present within the residue itself as opposed to instrument variability. Some instances of dissimilarity are also linked to the four samples which were reproduced for the second round of analysis, which could speak to the uncontrolled variation possible within production despite the employment of identical techniques and formulas.\u003c/p\u003e\u003cp\u003eWith the same spectral processing steps taken, the transformed data are less variable between the two machines than the raw data, with differences being generally minimal and not influencing material interpretation (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). One of the more evident differences present is that the low end of the ZnSe/TE-MCT-A spectral range was limited to 650 cm⁻\u0026sup1;, preventing the collection of data points to the same range as the KBr/LN-MCT-B as well as substantially increasing the noise present in the lower range of spectral limits within the ZnSe/TE-MCT-A-generated spectra. While this limitation was not drastic for the samples analyzed here, it could present a challenge when interpreting materials of other origins, particularly inorganic compounds such as mineral-based residues. Despite the minor differences observed between the spectra from each machine, visual group classification did not vary significantly and results were consistent with only 3 outliers (in the second classification attempt, 91\u0026thinsp;\u0026minus;\u0026thinsp;10 was assigned to Group 5 as opposed to Group 1; 91\u0026thinsp;\u0026minus;\u0026thinsp;12 was assigned to Group 3 as opposed to Group 2; and 91\u0026thinsp;\u0026minus;\u0026thinsp;02/36 was assigned to Group 5 as opposed to Group 3).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe results of the intra-instrument variability, tested on the ZnSe/TE-MCT-A system, showed that orientation changes induced spectral changes in some but not all samples tested. There is a clear differentiation between those that appear quite stable despite shifting the orientation of the sample (91\u0026thinsp;\u0026minus;\u0026thinsp;26 and 91\u0026thinsp;\u0026minus;\u0026thinsp;29), and those which appear sensitive to the changes (91\u0026thinsp;\u0026minus;\u0026thinsp;11). There was also some intermediate behavior observed among tested samples which contained clay as an admixture (91\u0026thinsp;\u0026minus;\u0026thinsp;28, 91\u0026thinsp;\u0026minus;\u0026thinsp;30, 91\u0026thinsp;\u0026minus;\u0026thinsp;32). This likely originates from the area selected for analysis, with the area analyzed for 91\u0026thinsp;\u0026minus;\u0026thinsp;11 containing a distinct vegetal fiber embedded within the resin mixture whereas 91\u0026thinsp;\u0026minus;\u0026thinsp;26 and 91\u0026thinsp;\u0026minus;\u0026thinsp;29 displayed more consistency in the residue surface despite being admixtures. 91\u0026thinsp;\u0026minus;\u0026thinsp;26 and 91\u0026thinsp;\u0026minus;\u0026thinsp;29 demonstrated high reproducibility across all orientations, exhibiting minimal pointwise difference with consistently low SDs and near-perfect correlation between the respective spectra. Cohen\u0026rsquo;s d values hovered around zero, further supporting that orientation had minimal effect on resulting spectra. In contrast, the spectra from 91\u0026thinsp;\u0026minus;\u0026thinsp;11 exhibited strong orientation-dependent variation, both in magnitude and shape. Spectra from 91\u0026thinsp;\u0026minus;\u0026thinsp;28, 91\u0026thinsp;\u0026minus;\u0026thinsp;30, and 91\u0026thinsp;\u0026minus;\u0026thinsp;32 were generally stable across orientations, though some pairings revealed modest variation in intensity or shape. These minor deviations may be attributed to subtle surface texture differences or interactions between the organic matrix and mineral inclusions. Such possible variability within a single area introduces substantial challenges for exact reproducibility, especially within archaeological contexts, and serves as a point of caution for generating diagnostic interpretations from limited test areas. These findings, supported by PCA results discussed in section \u003cspan refid=\"Sec5\" class=\"InternalRef\"\u003e3\u003c/span\u003e.c., underscore a key methodological gap: unlike other lithic analysis techniques such as illustration, no standard convention exists for orienting lithics in reflectance FTIR. We suggest that orientation should not be overlooked, and, at minimum, analysts should document sample positioning during measurement and for heterogenous residues, it may be advisable to collect spectra from multiple orientations. Alternatively, developing standard orientation protocols (e.g. aligning visible flow fabrics along a fixed axis) could help mitigate variability and improve reproducibility in archaeological applications.\u003c/p\u003e\n\u003ch3\u003e3.b. Classification Overview and Group-by-Group descriptions\u003c/h3\u003e\n\u003cp\u003eThe classification of the FTIR spectra into six distinct groups reflects meaningful compositional differences among the samples while also taking into consideration the role of visual identification skills from the analyst. Groupings appear to be primarily driven by the presence or absence of specific organic materials, with groups independently dominated by tree resins, beeswax, animal fat, ochre, or animal glues, and the patterning of their associated infrared absorption peaks. The diversity of functional groups provides a chemical basis for some of the observed classes, though the majority of variation lies in the fingerprint region.\u003c/p\u003e\u003cp\u003eAmidst the spectral differences that led to the unique classification groupings (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), there is some noticeable overlap in peak presence which indicates an incidence of similarity in material composition across the samples. This can be useful in addressing composite materials that display a conglomeration of peaks representative of different compositional elements. This study also helps to highlight the differences that can be present within such conglomerate materials, lending potential references for attempting to identify unknown adhesives based on presence or absence of known peaks. Noted differences in peak position between the two machines, though minor, may also indicate shifts that occur over time due to degradation of materials.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003ePrincipal wavenumber assignments and corresponding molecular interpretations for the six analyst-defined visual group classifications which were congruent between the two analysts working with spectra from both the KBr/LN-MCT-B and ZnSe/TE-MCT-A instruments. Differences in peak positions in wavenumber units between instruments are indicated in bold. This table includes the most prominent or diagnostically relevant peaks per group, with functional assignments and interpretive notes.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGroup\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWavenumber (cm⁻\u0026sup1;) \u0026ndash; KBr/LN-MCT-B\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eWavenumber (cm⁻\u0026sup1;) \u0026ndash; ZnSe/TE-MCT-A\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAssignment\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNotes\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e3400\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e3380\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eO-H and N-H stretch\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eBroad, common in phenols\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e2980\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e2945\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eC-H stretch\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAliphatic chains, possibly methyl groups\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2920\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2920\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eC-H stretch\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAromatics or aliphatic\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e2890\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e2870\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eC-H stretch\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAromatics or aliphatic\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2855\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2855\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eC-H stretch\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAliphatic CH₂ symmetric stretch\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1695\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1695\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eC\u0026thinsp;=\u0026thinsp;O stretch\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eCarboxylic acids, esters\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e1606\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e1604\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eC\u0026thinsp;=\u0026thinsp;C stretch\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAromatic double bonds\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e1516\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e1517\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eC\u0026thinsp;=\u0026thinsp;C stretch\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eCharacteristic of phenolic resins\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1464\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1464\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCH₂ bend\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAliphatic hydrocarbon\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e1456\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e1453\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCH₂ bend\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAliphatic chain deformation\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1435\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1435\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCH₂ bend\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAliphatic hydrocarbon\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e1386\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e1383\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCH3 bend\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAliphatic methyl deformation\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e1273\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e1276\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eC-O stretch\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eCarboxylic acids\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1238\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1238\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eC-O stretch\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eEsters or polysaccharides\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e1208\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e1211\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eC-O stretch\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePossible glycosidic or resin-derived\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1169\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1169\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eC-O-C or C-C stretch\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eTerpenoids, esters\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e1126\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e1124\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eC-O-C or C-C stretch\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eTerpenoids, resin acids\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1035\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1035\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSi-O stretch\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSilicates\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e833\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e833\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eN-O stretch\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePossible nitrate contamination or silicates\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e776\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e776\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSi-O bend\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSilicates\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2957\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2957\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eC-H stretch\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAliphatic\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2932\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2932\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eC-H stretch\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAliphatic\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2857\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2857\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eC-H stretch\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAliphatic\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e1733\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e1731\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eC\u0026thinsp;=\u0026thinsp;O stretch\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eEsters, ketones, carboxylic acids\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e1696\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e1695\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eC\u0026thinsp;=\u0026thinsp;O stretch\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eCarboxylic acids, esters\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1607\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1607\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eC\u0026thinsp;=\u0026thinsp;C stretch\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAromatic ring\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e1471\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e1473\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCH₂ bend\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAliphatic hydrocarbon\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1456\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1456\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCH₂ bend\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAliphatic chain deformation\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1386\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1386\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCH3 bend\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAliphatic methyl\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1276\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1276\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eC-O stretch\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eCarboxylic acid or esters\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1238\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1238\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eC-O stretch\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eEsters or polysaccharides\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1179\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1179\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eC-O stretch\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePossible phenol or terpene-related\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1035\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1035\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSi-O stretch\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSilicates\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3400\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3400\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eO-H stretch or N-H stretch\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eBroad, often in gums or proteins\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e2967\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e2965\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eC-H stretch\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAliphatic methyl\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e2940\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e2938\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eC-H stretch\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAliphatic CH₂\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2856\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2856\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eC-H stretch\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAliphatic CH₂ symmetric stretch\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1715\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1715\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eC\u0026thinsp;=\u0026thinsp;O stretch\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eCarboxylic acids\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1695\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1695\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eC\u0026thinsp;=\u0026thinsp;O stretch\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eCarboxylic acids or aged resins\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e1607\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e1603\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eC\u0026thinsp;=\u0026thinsp;C stretch\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAromatic ring\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1516\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1516\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eC\u0026thinsp;=\u0026thinsp;C stretch\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePhenolic or aromatic\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1464\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1464\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eC-H bend\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAliphatic chain\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e1455\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e1453\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eC-H bend\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAliphatic\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e1435\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e1431\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCH₂ bend\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAliphatic hydrocarbon\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e1274\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e1276\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eC-O stretch\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eCarboxylic acids or esters\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1237\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1237\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eC-O stretch\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eCarbohydrates or esters\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1210\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1210\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eC-O stretch\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eGum or resin linkages\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1124\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1124\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eC-O-C or C-C\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePossible glycosidic or resin-derived\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1034\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1034\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSi-O, C-O or C-C\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSilicates, or possible terpenoids or polysaccharides\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e856\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e856\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAromatic out-of-plane\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePossible aromatic bending\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e819\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e819\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAromatic or skeletal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eOut-of-plane bend\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3350\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3350\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eO-H stretch, N-H stretch\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eCollagen, proteinaceous material\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e3088\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e3075\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eC-H, C\u0026thinsp;=\u0026thinsp;O, or N-H stretch\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePossible Amide overtones\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1660\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1660\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eC\u0026thinsp;=\u0026thinsp;O stretch, N-H stretch\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAmide I\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1565\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1565\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eC\u0026thinsp;=\u0026thinsp;O stretch, N-H stretch\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAmide II\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1558\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1558\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eC\u0026thinsp;=\u0026thinsp;O stretch, N-H stretch\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAmide II\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e1456\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e1455\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCH₂, N-H bend\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eProtein backbone\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1283\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1283\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eP\u0026thinsp;=\u0026thinsp;O\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePhosphorylated proteins\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1242\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1242\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eP\u0026thinsp;=\u0026thinsp;O\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eCollagen\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e1205\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e1200\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eC-O stretch\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAmide III\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e1159\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e1154\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eC-N or C-O stretch\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAmide III or carbohydrates\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1080\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1080\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSi-O stretch\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSilicates\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e1033\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e1027\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSi-O or C-O stretch\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSilicates or polysaccharides\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e973\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e973\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eC-H deformation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eLipids or hydrocarbons\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e718\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e718\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCH₂ rocking\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAliphatic chains\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2921\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2921\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eC-H stretch\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAliphatic hydrocarbon\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2852\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2852\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eC-H stretch\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAliphatic hydrocarbon\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e1739\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e1737\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eC\u0026thinsp;=\u0026thinsp;O stretch\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eEsters\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1696\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1696\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eC\u0026thinsp;=\u0026thinsp;O stretch\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eDegraded fats or mixed esters\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1652\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1652\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eC\u0026thinsp;=\u0026thinsp;C or Amide I\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePossible unsaturated fats or proteins\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e1558\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e1561\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1473\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1473\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCH₂ scissor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eLipids\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1464\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1464\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCH₂ scissor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eLipids\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e1386\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e1385\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCH3 bend\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eLipids\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1247\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eC-O\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eEsters\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e3400\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e3375\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eO-H stretch\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePolysaccharides, gums\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e2950\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e2930\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eC-H stretch\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePolysaccharide ring systems\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e2900\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e2880\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eC-H stretch\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSugars\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1610\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1610\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCOO- stretch\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eCarboxylate salt, gums\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1505\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1505\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eC\u0026thinsp;=\u0026thinsp;C or NH bend\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eWeak aromatic or proteins\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e1240\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e1250\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eC-O stretch\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePolysaccharides or ester\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1080\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1080\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSi-O stretch\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSilicates\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1040\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1040\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eC-O-C stretch\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePolysaccharides, gums\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e980\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e980\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eC-H deformation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eDegraded organics\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eSome specimens yielded multiple spectral classifications from different test points, attributed to the presence of inclusions from additives that strongly swayed the spectral results. Of the 31 samples included, all 31 were assigned to defined classification groups (1\u0026ndash;6), with 11 showing additional absorption peaks beyond those used to define their grouping. Materials that are thin or translucent present particular difficulties. In this case, they often produced spectra which showed overlapping features representative of both the residue and the lithic substrate, resulting in noisier and potentially misleading outputs. Transparent adhesives were exclusive to Groups 4 and 6, implying that substrate interference can be particularly strong in these cases. This highlights the risk of misinterpreting translucent, small, or thin residues and emphasizes the need for extensive testing of background substrates to correctly identify adhesive materials.\u003c/p\u003e\u003cp\u003eGroup 1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) consists primarily of spruce-based mixtures, often modified with ochre, clay, or sand. This group is characterized by a dense distribution of peaks across the mid-IR spectrum, with prominent peaks present in the functional group zone at about 3600\u0026thinsp;\u0026minus;\u0026thinsp;3250, 2950\u0026thinsp;\u0026minus;\u0026thinsp;2850, 1690, and 1600 cm⁻\u0026sup1;. Other prominent peaks are found within the fingerprint region, located at about 1515, 1465, 1425, 1370, 1275, 1235, 1210, 1170\u0026thinsp;\u0026minus;\u0026thinsp;1125, and 1035 cm⁻\u0026sup1;. The peaks observed in this group tend to correspond to aliphatic C\u0026ndash;H stretching and bending, C\u0026thinsp;=\u0026thinsp;O stretching from esters, ketones, or carboxylic acids, aromatic C\u0026thinsp;=\u0026thinsp;C stretches, acidic C-O stretching, and various inorganic additives (Edwards et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e1996\u003c/span\u003e; Edwards and Falk \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e1997\u003c/span\u003e; Socrates \u003cspan citationid=\"CR130\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Vahur et al. \u003cspan citationid=\"CR137\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Bruni and Guglielmi, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Helwig et al. \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Duce et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Monnier 2017a; Martin-Ramos et al. \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The wide array of peaks in this group supports complex, multi-component formulations with both organic and mineral components. Comparisons to reference spectra yielded some correlations to colophony from the INFRA-ART library, modern \u003cem\u003ePistacia\u003c/em\u003e resin from the Kimmel library, and spruce resin on English flint from University of Minnesota Archaeological Materials Infrared Spectra library.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eGroup 2 (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e) includes samples composed mainly of spruce resins combined with beef fat and/or clay, as well as pure birch tar. These samples exhibit peaks similar to Group 1 in the 1700\u0026ndash;1450 cm⁻\u0026sup1; region, but with notable differences in peak shape and intensity\u0026mdash;particularly at about 2960, 2930, 1735, and 1700 cm⁻\u0026sup1; in the functional group region, and 1470, 1450, 1385, 1250, and 1175 cm⁻\u0026sup1; within the fingerprint region. The peaks observed in this group tend to correspond with aliphatic C-H stretching and bending (Edwards 1996; Edwards and Falk \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e1997\u003c/span\u003e; Socrates \u003cspan citationid=\"CR130\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Beltran et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Martin-Ramos et al. \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), C\u0026thinsp;=\u0026thinsp;O stretching from esters, ketones, or carboxylic acids (Edwards 1996; Cinta-Pinzaru et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Monnier et al. \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2017a\u003c/span\u003e), acidic C-O stretching (Socrates \u003cspan citationid=\"CR130\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Cinta-Pinzaru et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Monnier et al. \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2017a\u003c/span\u003e; Schmidt et al. \u003cspan citationid=\"CR121\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Schmidt and Koch \u003cspan citationid=\"CR119\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), and a minor phosphate peak at 1035 cm⁻\u0026sup1;. The presence of beef fat and flax may account for the unique combination of vibrations, and the relatively reduced number of total peaks suggests less chemically diverse compositions compared to Group 1 (Edwards 1996; Socrates \u003cspan citationid=\"CR130\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Boeriu et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Bruni and Guglielmi \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Cinta-Pinzaru et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Monnier et al. \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2017a\u003c/span\u003e; Beltran et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Chambre and Dochia \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Schmidt et al. \u003cspan citationid=\"CR121\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). When assessed alongside reference libraries, similarities to modern tree resins (\u003cem\u003ePistacia\u003c/em\u003e and spruce) from both the Kimmel library and University of Minnesota Archaeological Materials Infrared Spectra Library, and rosin from University of Minnesota Archaeological Materials Infrared Spectra Library, were apparent in both the functional group and fingerprint regions.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eGroup 3 (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e) is defined by a strong dominance of pine resin, either pure or in combination with additives like beeswax, charcoal, flax, or ochre. The spectra in this group are similar to groups 1 and 2 in the 3400 cm⁻\u0026sup1; area, and have tightly clustered peaks in the functional group region at 2967, 2940, 2856 cm⁻\u0026sup1; as well as a characteristic region of diagnostic peaks present at about 1735, 1695, 1600, 1515, 1435, 1375, 1280, 1235, 1130, and 1040 cm⁻\u0026sup1;. These peaks reflect resin-derived aliphatic and aromatic terpenoids and carboxylic acids (Edwards 1996; Edwards and Falk \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e1997\u003c/span\u003e; Socrates \u003cspan citationid=\"CR130\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Mazza et al. \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Bruni and Guglielmi \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Helwig et al. \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Beltran et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Monnier et al. \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2017a\u003c/span\u003e; Martin-Ramos et al. \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Schmidt et al. \u003cspan citationid=\"CR122\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), with possible modifications arising from thermal alteration (charcoal) or additives such as beeswax, flax, and ochre (Socrates \u003cspan citationid=\"CR130\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Boeriu et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Vahur et al. \u003cspan citationid=\"CR137\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Diefendorf et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Duce et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Chambre and Dochia \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Das et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Schmidt et al. \u003cspan citationid=\"CR122\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Comparison to reference libraries yielded similarities to modern tree resins (\u003cem\u003ePistacia\u003c/em\u003e and spruce) from the Kimmel Library and University of Minnesota Archaeological Materials Infrared Spectra Library, respectively.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eGroup 4 (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e) encompasses all the proteinaceous glues: commercial and experimentally made sinew glue, bone glue, and hide glues. This group displays a very different spectral profile from the others, characterized by unique peaks at higher frequencies including at about 3330, 3080, and 2950 cm⁻\u0026sup1;, associated with O\u0026ndash;H, N\u0026ndash;H and C-H stretching vibrations. The dominance of amide I and II bands around 1650 and 1550 cm⁻\u0026sup1; reflects the collagen-based nature of these materials, clearly setting this group apart in classification, along with phosphate signatures at about 1280, 1240, 1200, 1030, and 980 cm⁻\u0026sup1; (Socrates \u003cspan citationid=\"CR130\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Chadefaux et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Vahur et al. \u003cspan citationid=\"CR137\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Nunziante-Cesaro and Lemorini \u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Helwig et al. \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Solodenko et al. \u003cspan citationid=\"CR131\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Bradtmoller et al. 2016; Monnier et al. \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e2017b\u003c/span\u003e; Monnier and May \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). When compared to reference libraries, there were strong similarities to bone and hide glue from INFRA-ART\u0026rsquo;s library, as well as gelatin and leather from the Kimmel library. Similarities to silica were also present, indicating the high influence from the flint substrate.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eOf all groupings, Group 5 (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e) is the most heterogenous; chemically diverse but unified by the common use of spruce resin, beeswax, or acacia gum, with beeswax being the unifying element. Samples in this group often contain mixtures, such as spruce with beeswax or ochre, or acacia gum mixed with beeswax. Peaks at 2950, 2920, 2850, 1740, 1730, 1695, 1470, 1460, and 1240 cm⁻\u0026sup1; are consistent across the group, likely corresponding to aliphatic and ester groups from beeswax, common lipids, as well as trace polysaccharide residues from gum (Edwards 1996; Regert et al. \u003cspan citationid=\"CR109\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Socrates \u003cspan citationid=\"CR130\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Martin-Ramos et al. \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Chasan et al. 2021; Das et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The presence of both plant and animal-based components in various ratios accounts for the spectral complexity found here. Reference library comparisons yielded strong similarities to waxes, including beeswax (INFRA-ART) and paraffin (Kimmel), in both the functional group and fingerprint regions.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eGroup 6 (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e) is represented by a single sample composed of pure acacia gum. This group has notable peaks at about 2930, 2880, 1610\u0026thinsp;\u0026minus;\u0026thinsp;1600, 1420, 1250\u0026thinsp;\u0026minus;\u0026thinsp;1230, and 1080\u0026thinsp;\u0026minus;\u0026thinsp;1040 cm⁻\u0026sup1;. The absence of strong protein or resin-associated peaks present in the other groupings results in a sparse spectral fingerprint, explaining its isolated position in analyst classification and PCA. Its unique polysaccharide signature may contribute to distinctive absorption features not widely shared with other groups beyond the brief appearance within Group 5, presented by the beeswax-acacia gum admixture (Edwards et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e1996\u003c/span\u003e; Socrates \u003cspan citationid=\"CR130\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Martin-Ramos et al. \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Thombare et al. \u003cspan citationid=\"CR133\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eIn analyzing the classification results, five of the 31 samples emerge as notable outliers, either due to their hybrid compositions, unusual spectral signatures, or incongruities between their chemical makeup and assigned groupings. One such case is Sample 91\u0026thinsp;\u0026minus;\u0026thinsp;10, which contains a complex mixture of \u003cem\u003ePinus nigra\u003c/em\u003e, beeswax, and charcoal. While its substantial \u003cem\u003ePinus\u003c/em\u003e component would suggest alignment with Group 3, which includes other pinus-based adhesives, this piece was classified into Group 1 by the first analyst, with Group1 being typically dominated by spruce and mineral additives like ochre, sand, or clay. This may indicate that the presence of charcoal significantly altered the infrared absorption characteristics, drawing its peak profile closer to that of Group 1 materials. However, the classification by the second analyst placed 91\u0026thinsp;\u0026minus;\u0026thinsp;10 within Group 5, which coincides with its beeswax content. Similarly, Sample 91\u0026thinsp;\u0026minus;\u0026thinsp;14, composed of \u003cem\u003ePinus\u003c/em\u003e, beeswax, and ochre, is placed in Group 5 rather than Group 3. Like 91\u0026thinsp;\u0026minus;\u0026thinsp;10, its high beeswax and ochre content might mask or modulate the organic peaks typically associated with pine resins, resulting in a spectral shift and making it a transitional case between the two groups.\u003c/p\u003e\u003cp\u003eAnother ambiguous case is Sample 91\u0026thinsp;\u0026minus;\u0026thinsp;12, a blend of pine resin, beeswax, and flax. It shares a similar profile with several Group 3 samples, including 91\u0026thinsp;\u0026minus;\u0026thinsp;11 and 91\u0026thinsp;\u0026minus;\u0026thinsp;13, yet was initially categorized in Group 2. This suggests that the inclusion of flax, rich in plant fiber and possibly influencing ester and alcohol regions of the spectrum, may have introduced variability that aligned it more closely with other flax- or fat-containing mixtures typically found in Group 2 (Boeriu et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Monnier et al. \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2017a\u003c/span\u003e; Chambre and Dochia \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Das et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). During the second classification, 91\u0026thinsp;\u0026minus;\u0026thinsp;12 was placed within Group 3, more closely aligning it with the other pine-dominant samples, with notable similarities to Group 5 in the functional group zone but with enough similarities to Group 3 between 1800\u0026thinsp;\u0026minus;\u0026thinsp;700 cm⁻\u0026sup1; range to warrant its final placement. The shift in classification despite comparable base components points to the nuanced influence of additives on spectral clustering as well as the role of analyst choices.\u003c/p\u003e\u003cp\u003eThermal processing also appears to play a critical role in group differentiation. Sample 91\u0026thinsp;\u0026minus;\u0026thinsp;25, composed of spruce and beef fat subjected to frequent heating, is classed in Group 5, even though other similarly composed samples (91\u0026thinsp;\u0026minus;\u0026thinsp;22 and 91\u0026thinsp;\u0026minus;\u0026thinsp;24) are found in Group 2. The act of repeated heating may have chemically altered its composition through oxidation or carbonization resulting in distinguishable changes in the infrared spectrum, particularly in the C\u0026thinsp;=\u0026thinsp;O and C\u0026ndash;H stretch regions (Duce et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Schmidt et al. \u003cspan citationid=\"CR120\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Diefendorf et al. 2021). Its classification divergence underlines the importance of considering preparation methods in FTIR-based adhesive analysis.\u003c/p\u003e\u003cp\u003eSample 91\u0026thinsp;\u0026minus;\u0026thinsp;06, a 1:1 mixture of acacia gum and beeswax, also occupies a liminal space. While its beeswax content aligns with Group 5, the presence of acacia gum introduces a polysaccharide-rich signature more characteristic of Group 6, which consists only of 91\u0026thinsp;\u0026minus;\u0026thinsp;05. The latter, composed of pure acacia gum, is chemically and spectrally distinct from all other samples. As such, 91\u0026thinsp;\u0026minus;\u0026thinsp;05 serves within this dataset as a clear outlier and a compositional benchmark for plant gum-based adhesive systems. It illustrates the strong differentiation that pure gum exhibits compared to resin, fat, or protein-based adhesives.\u003c/p\u003e\u003cp\u003eThese outlier samples highlight the fluidity and complexity of classifying archaeological adhesives of unknown compounds based on FTIR data. They underscore how both material composition and post-depositional or preparatory processes such as mixing or heating can influence spectral outcomes. At the same time, their identification draws attention to the role of grouping itself as an analytical strategy. The dataset analyzed here was not originally structured in terms of groups, but groupings were applied as a means of visualizing relationships and exploring variation, reflecting a broader interpretive choice to treat compositional similarities or differences as analytically significant. This underscores not only the importance of refining group definitions in specific cases, but also of critically reflecting on how such strategies shape future interpretive work on adhesive technologies.\u003c/p\u003e\n\u003ch3\u003e3.c. Principal Component Analysis\u003c/h3\u003e\n\u003cp\u003eA core question addressed is whether PCA, as an exploratory tool for visualizing variability in spectral data, can reveal trends that align with groupings identified by analysts, and whether such methods would be transferable to archaeological adhesives. Statistical analysis techniques were incorporated to better visualize and interpret the spectral data, addressing potential limitations of visual classification which can be subject to analyst choice, bias and skill. These exploratory PCA results show that instrument-specific characteristics subtly influence the structure and effectiveness of the multivariate models, even despite sample composition and preprocessing steps remaining consistent. Both ZnSe/TE-MCT-A and KBr/LN-MCT-B datasets were preprocessed identically but yielded markedly different results in terms of group separation and explained variance.\u003c/p\u003e\u003cp\u003eAdding a first derivative transformation to the ZnSe/TE-MCT-A dataset \u0026ndash; a step that minimized the effect of baseline differences \u0026ndash; substantially improved the clarity and compactness of class separation in PCA space, even though the raw spectra already exhibited fairly stable baselines. Rather than primarily correcting baseline drift, the derivative enhanced gradual absorbance trends and inflection points, converting subtle slope changes in the raw spectra into discrete spectral trends that aided in discriminating chemically similar adhesives. While SNV and GLSW alone partially corrected for scatter effects and sample variability, they were less effective in resolving overlapping adhesive classes. Importantly, while the ZnSe/TE-MCT-A spectra did not always suffer from severe baseline drift, the application of a derivative still enhanced discrimination by amplifying chemically meaningful slope features, particularly in overlap-prone regions. The resulting PCA plots demonstrate clear separation among all adhesive classes, with reduced intra-group dispersion and greater inter-group distances, especially between spruce-based, protein-based, and pinus-based adhesives. These improvements suggest that derivative preprocessing reveals subtle yet chemically meaningful spectral differences that are otherwise masked by baseline variation or low-frequency noise. Although the explained variance by the first PCs remains modest, this was not the explicit objective of preprocessing. An alternative strategy might have been to maximize explained variance by selectively removing spectral regions or tailoring preprocessing toward the first few PCs. However, the current approach shows that such steps were unnecessary \u0026ndash; meaningful visual groupings and improved class interpretability were achieved without deliberately inflating variance capture. This supports the use of a first derivative as a beneficial preprocessing step for ZnSe/TE-MCT-A-based FTIR data in archaeological residue applications.\u003c/p\u003e\u003cp\u003eTo evaluate the discriminative power of FTIR data processed via PCA, two score plots were generated from the same preprocessed dataset: one labeled according to known adhesive composition, and the other based on analyst-assigned groupings without prior knowledge of adhesive types (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e). Both plots reveal at least some overlap among certain adhesives classes, particularly spruce-based, gum-based, and pinus-based mixtures, yet also show distinct separation for more compositionally unique adhesives. Protein-based samples exhibit clear clustering in both plots, while birch tar also forms a separate grouping, suggesting that these adhesives possess sufficiently distinct spectral signatures to be reliably identified via PCA.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe analyst-defined approach allowed for exploratory assessment of natural clustering tendencies in the data, but the resulting plots showed substantial overlap and inconsistent boundaries, particularly among resin-based samples. By contrast, the adhesive-defined classification provided an objective framework more grounded in material composition and produced clearer separation for chemically distinct groups with mixtures clustering more tightly under their compositionally defined classes than under analyst-derived labels. Although both methods revealed meaningful structure, the adhesive-based groupings ensured consistent and reproducible class definitions, particularly important given the inclusion of composite adhesives with mixed chemical origins. These mixtures often produced spectra that spanned or blended characteristic features from multiple materials, complicating visually derived classifications. For this reason, the adhesive-defined model was selected for application to the validation set, as it offered a more transparent and chemically grounded basis for evaluating model performance and assessing the reproducibility of spectral patterns across instruments and analytical conditions.\u003c/p\u003e\u003cp\u003eConstructing the training set using adhesive-defined classes that were based on the known chemical composition of each experimental residue offered significant advantages over relying on analyst-defined groupings derived from spectral inspection alone. Adhesive-based classification ensured that the grouping criteria were grounded in actual material properties rather than subjective visual interpretation, reducing the risk of bias and enhancing the model's validity. This approach also allowed for more accurate evaluation of spectral reproducibility and class discriminability, as each sample could be reliably assigned to a chemically distinct category. In contrast, analyst-defined groupings, while useful for exploratory clustering, often grouped samples based on dominant spectral features that did not always align with underlying primary compositional differences, especially in cases involving complex mixtures or overlapping functional groups. By anchoring the training set in compositional reality, the adhesive-defined model provided a more objective and interpretable framework for multivariate analysis, which is particularly important for future application to unknown archaeological residues.\u003c/p\u003e\u003cp\u003eApplying PCA to both raw and KKT-processed data allows for a comparative assessment of how preprocessing influences the structure and interpretability of FTIR spectral datasets. PCA on raw spectra retains the full spectral signal, including baseline variation, reflectance effects, and instrument-specific noise, offering a realistic view of the inherent variability present in the original measurements. This is particularly useful for evaluating intra- and inter-instrument consistency, as well as identifying outliers or systematic differences due to sample presentation or acquisition conditions. In contrast, applying PCA to KKT-processed spectra emphasizes subtle peak shifts and shape differences while minimizing baseline drift and multiplicative effects. This enhances the ability to resolve chemically relevant features and differentiate between adhesive classes, especially those with overlapping spectral profiles. Together, these analyses provide a more comprehensive understanding of spectral variability; raw reflectance PCA reveals the challenges of direct interpretation under real-world conditions, while PCA on transformed reflectance spectra highlights the latent structure accessible through optimized preprocessing.\u003c/p\u003e\u003cp\u003eInterestingly, PCA projections revealed that raw ZnSe/TE-MCT-A spectra produced clearer class separation than their fully preprocessed counterparts, whereas raw KBr/LN-MCT-B spectra required more extensive preprocessing to achieve meaningful clustering (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003e). This contrast reflects underlying differences in spectral data quality between the two instruments. When unprocessed, ZnSe/TE-MCT-A spectra were generally high in signal-to-noise ratio and exhibited stable baselines, allowing key chemical features to be preserved even in minimally processed form. In this case, aggressive preprocessing steps such as derivative transformation and GLSW may have inadvertently suppressed or distorted class-relevant variation, reducing interpretability. Conversely, raw KBr/LN-MCT-B spectra were more affected by baseline shifts, scattering, and sample presentation artifacts, which obscured chemically meaningful patterns. Preprocessing, particularly SNV and GLSW, was therefore essential to reduce noise and emphasize diagnostic features, resulting in improved class clustering. These findings highlight the importance of instrument-specific preprocessing strategies and suggest that raw spectral quality should guide the intensity of data transformation applied prior to multivariate analysis.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003ePCA of the raw, truncated spectra revealed distinct differences in how adhesive classes are distributed across the ZnSe/TE-MCT-A and KBr/LN-MCT-B datasets. In the ZnSe/TE-MCT-A PCA plot, adhesive groups such as protein-based, spruce-based, and pinus-based classes clustered clearly along PC1 and PC2, indicating that even without extensive preprocessing, the ZnSe/TE-MCT-A spectra retained strong chemically driven variance. This suggests a high baseline quality and signal stability inherent to the ZnSe/TE-MCT-A instrument, allowing compositional differences among adhesives to emerge naturally in PCA space. In contrast, the KBr/LN-MCT-B raw data exhibited more compression and overlap among adhesive classes, with only the protein-based and gum-based samples forming somewhat distinct clusters. The tighter clustering and reduced spread along both principal components suggest that KBr/LN-MCT-B spectra in raw form contain more baseline variation and scatter-related noise, which obscures fine-grained chemical distinctions. In contrast, when smoothed and KKT processed data were analyzed, KBr/LN-MCT-B spectra demonstrated improved separation between adhesive classes, while ZnSe/TE-MCT-A results were comparable or slightly less resolved. These results highlight the importance of developing a tailored preprocessing strategy that accounts for both the specific characteristics of the instrument and the nature of the samples being analyzed. In this study, preprocessing proved especially important for the KBr/LN-MCT-B-generated data, whereas the ZnSe/TE-MCT-A-generated data retained more interpretable chemical features even in their raw form. However, this outcome should not be generalized across all cases. Rather, it underscores the need for a flexible, iterative approach informed by testing against reference samples. Effective preprocessing is not one-size-fits-all, rather it depends on the optical configuration, detector type, and sample properties, and must be optimized accordingly.\u003c/p\u003e\u003cp\u003eExpanding the KBr/LN-MCT-B dataset to include the lower wavenumber region (down to 400 cm⁻\u0026sup1;) had a modest but noticeable effect on PCA group structure (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003e). Notably, the added inclusion of the 650\u0026thinsp;\u0026minus;\u0026thinsp;400 cm⁻\u0026sup1; region did not drastically alter major groupings but appeared to refine within-group structure, particularly among plant resin classes (spruce- and pinus-based), which remained partially overlapping yet slightly more compact in the extended model. These results suggest that, at least for the KBr/LN-MCT-B data, the core chemical distinctions between adhesives are already well-represented in the 1800\u0026thinsp;\u0026minus;\u0026thinsp;650 cm⁻\u0026sup1; region, but the extended range may help stabilize clustering and support classification by providing subtle inorganic-related features not captured above 650 cm⁻\u0026sup1;.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThese results suggest that while PCA can help visualize certain spectral trends, particularly in chemically distinct groups, it remains limited in resolving complex or compositionally overlapping mixtures without prior classification or expert intervention. Visual inspection remains essential as the most reliable method of identification and classification, and multiple spectra per sample are necessary for accurate identification due to the complex nature of composite materials as well as the presence of inclusions or contaminants on spectral reliability. PCA serves as a valuable first step in processing and visualizing trends in spectral data; however, it cannot replace expert interpretation. In this case, results remain somewhat ambiguous, emphasizing the need for alternative processing and subsequent analysis.\u003c/p\u003e\u003cp\u003eHCA was conducted using both preprocessed spectral data directly and PCA-reduced data, allowing comparison of clustering outcomes under different dimensionality conditions. Preprocessing involved KKT transformation, truncation, smoothing, SNV correction, and GLSW weighting, with analyses of the ZnSe/TE-MCT-A datasets further incorporating a first derivative transformation. Across approaches, Ward\u0026rsquo;s method was employed as the linkage criterion to optimize the formation of compact clusters.\u003c/p\u003e\u003cp\u003eWhen HCA was performed directly on the preprocessed spectra without PCA, the model produced a very detailed branching structure, splitting samples into many small sub-clusters. This captured subtle spectral differences but also created \u0026ldquo;clustering noise\u0026rdquo;, with closely related adhesives sometimes scattered across multiple branches rather than grouped together, particularly in samples with overlapping or composite compositions. In contrast, HCA on PCA-reduced data (using either 3 or 5 principal components) yielded more coherent and interpretable cluster structures, aligning more closely with known adhesive groupings such as spruce-based, pinus-based, proteinaceous glues, and gums. Notably, the application of the first derivative prior to PCA improved separation between clusters by enhancing slope-related spectral features, reducing within-cluster variance.\u003c/p\u003e\u003cp\u003eFurthermore, subsampling the dataset by including every other sample or a single representative spectrum per sample helped minimize redundancy-driven clustering biases. This reduction in dataset density led to more distinct cluster boundaries, particularly in PCA-informed HCA (Fig.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e13\u003c/span\u003e), where the variance-weighted distance between cluster centers increased, indicating stronger inter-group differentiation. Overall, PCA-informed HCA with 3 PCs offered a balanced approach, maximizing class separation while reducing noise, especially for complex adhesive mixtures. The resulting dendrograms showed a structure that broadly captured the expected relationships among adhesive types, demonstrating that this method not only reduced noise but also yielded chemically meaningful groupings\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\n\u003ch3\u003e3.d. Validation Results\u003c/h3\u003e\n\u003cp\u003eThe projection of the validation set which was composed of pure materials including beeswax, spruce resin, acacia gum, and pine resin into the existing PCA models reveals only partial consistency with the structure established by the training data (Fig.\u0026nbsp;\u003cspan refid=\"Fig14\" class=\"InternalRef\"\u003e14\u003c/span\u003e). Importantly, this validation test is not intended as a traditional accuracy check, but rather as a stress test to evaluate limits and behavior of the model when applied to fundamentally different data types. Through that lens, the results expose important limitations in classification confidence. A few validation samples, particularly those derived from spruce resin, do plot within or near their expected clusters, especially when applied to raw spectral data. However, the behavior of the other samples is inconsistent with what is expected assuming that the primary adhesive component governs the distribution of clusters within the PCA space. Here, pure pine resin clusters closely with the spruce-based adhesive group, a feature not seen in the training data, and pure beeswax appears near the pinus-based mixtures. While it is difficult to interpret with full certainty why these inconsistencies arise, these results suggest that the PCA model is not isolating the dominant component of each adhesive mixture as a distinct signal but is instead capturing spectral characteristics of shared ingredients or broader compositional similarities. For example, the overlap between pure beeswax and pinus-based adhesives may reflect the inclusion of beeswax in many of those mixtures, highlighting PCA\u0026rsquo;s tendency to amplify patterns tied to shared components rather than to isolate patterns related to individual substance types.\u003c/p\u003e\u003cp\u003eMore broadly, some pure validation samples occupy ambiguous positions between previously defined clusters or fall closer to material groupings that do not coincide with their known composition. The convergence of pure spruce and pure pine resins within the spruce-based adhesive cluster likely reflects chemical similarities between the two resins, reinforcing that PCA is sensitive to overall compositional resemblance. Notably, the mixed adhesive samples used for model calibration are more cleanly separated in PCA space than their pure counterparts, underscoring the model\u0026rsquo;s tuning toward composite patterns. These findings emphasize the limitations of PCA-based pattern detection when applied to chemically simple or unfamiliar inputs within the tested model. The presence of substantial misalignment, even within a controlled set of pure materials, raises concerns about the model\u0026rsquo;s capacity to reliably classify more complex archaeological residues. This analysis demonstrates that while PCA captures meaningful patterns, it does not reliably extract or differentiate individual components from within complex mixtures. Accordingly, classifications derived from PCA trends should be treated as suggestive rather than definitive, particularly in archaeological contexts where material composition is unknown, requiring validation through direct spectral comparisons, reference libraries, and complementary analytical methods. Without such safeguards, there is a risk of overinterpreting PCA plots and drawing unsupported conclusions from proximity alone, emphasizing the need for a cautious interpretive approach.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe HCA dendrogram showed somewhat clearer structure. Both beeswax samples clustered together, as did the two acacia gum samples, suggesting that these pure substances maintain distinct spectral signatures under the applied preprocessing conditions. The pine resin samples formed a broader but coherent cluster, while the spruce resin samples were positioned adjacent to but slightly distinct from pine, reflecting expected chemical similarities yet allowing some differentiation. The clustering distance between gum, wax, and resins indicates that the model could broadly distinguish between major adhesive categories, although within-resin differentiation remained modest. The HCA was significantly more well defined when applied to the PCA-reduced data.\u003c/p\u003e\u003cp\u003eAs an added validation measure and to further evaluate the effect of sample orientation, spectra from rotated samples were also projected onto the ZnSe/TE-MCT-A PCA model. The PCA results (Fig.\u0026nbsp;\u003cspan refid=\"Fig15\" class=\"InternalRef\"\u003e15\u003c/span\u003e) revealed distinct clustering patterns consistent with prior analyses but also highlighted the influence of both orientation and composition. All rotated spectra from sample 91\u0026thinsp;\u0026minus;\u0026thinsp;11 formed a tight and isolated cluster on the far right of PC1, demonstrating that even highly orientation-sensitive samples may cluster consistently in PCA space, suggesting that the dominant variation lies outside the primary principal components. Most spectra from spruce-based samples, especially 91\u0026thinsp;\u0026minus;\u0026thinsp;26, 91\u0026thinsp;\u0026minus;\u0026thinsp;28 and 91\u0026thinsp;\u0026minus;\u0026thinsp;29, clustered tightly regardless of orientation, reinforcing their high intra-sample reproducibility. However, samples 91\u0026thinsp;\u0026minus;\u0026thinsp;30 and 91\u0026thinsp;\u0026minus;\u0026thinsp;32 exhibited notable internal dispersion. While some spectra from each sample aligned closely with their respective clusters, other spectra from the same sample were displaced along PC1, indicating subtle but meaningful variation across orientations. This intra-sample spread suggests that microstructural or compositional heterogeneity, possibly due to layering effects, clay particle alignment, or other interactions between the matrix and additives, introduced variation detectable by PCA despite identical adhesive composition. These findings emphasize the importance of considering both sample orientation and material composition when interpreting clustering patterns in multivariate FTIR analysis.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\n\u003ch3\u003e3.e. Comparison of Reflectance-mode to Transmission-mode Spectra\u003c/h3\u003e\n\u003cp\u003eHere, reflectance-mode FTIR successfully enabled the detection of key spectral features in the experimental adhesives, particularly after KKT. Once transformed, the spectra exhibited strong alignment in peak position with corresponding transmission-mode spectra from external reference libraries, specifically in the fingerprint regions, supporting their comparability for qualitative analysis. This provides further evidence that with appropriate processing, spectra collected in reflectance-mode can be meaningfully interpreted using transmission-based reference standards. Despite this success, spectral quality in the reflectance spectra remained lower overall, even with the incorporation of 400 co-added scans per spectrum, likely due to the influence of surface roughness, matrix heterogeneity, and other optical effects. Important for further considerations is that most established reference libraries are built from transmission-mode data, which is typically collected on pure compounds under controlled conditions and without the constraints of non-destructive sampling requirements. This presents a challenge when interpreting reflectance-mode data from complex, heterogenous residues on archaeological materials. In the study here, while only a few exact spectral matches were found in existing libraries likely due to the composite and variable nature of the sample materials, major peaks allowed for moderate-level classification of adhesive types such as resins, waxes, gums, and proteinaceous materials through visual grouping (Fig.\u0026nbsp;\u003cspan refid=\"Fig16\" class=\"InternalRef\"\u003e16\u003c/span\u003e). These results underscore the need for additional experimental reference materials, particularly those prepared on lithic substrates, to improve identification accuracy and increase the interpretive value of non-destructive FTIR in archaeological applications.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e4.a. Spectral Reproducibility\u003c/span\u003e\u003c/p\u003e\u003cp\u003eInstrumental variation is a well-known but little addressed challenge in FTIR spectroscopy, particularly when comparing reflectance-mode data across platforms with differing optical hardware. Notably, KKT without normalization yielded the best performance in intensity-based metrics. These results support the application of KKT and amplitude normalization in cross-instrument studies and underscore the need for transparency in processing choices. Results further support the viability of FTIR as a successful method for interpreting various organic materials in the archaeological record, particularly as related to adhesive materials. The variations encountered in spectral uniformity are to be expected to an extent as a result of instrument specific differences, but are, for the most part, either minor or in part remedied through processing techniques which result in spectra that can be cross-referenced without difficulty. This study also supports the need for extensive and accessible reference libraries as a field-wide standard to increase the reproducibility of testing and ensure materials are being reliably classified.\u003c/p\u003e\u003cp\u003eWhile minor variation in peak location was observed between instruments, it was usually not far outside of the expected range. It is possible that the observed variation in peak location and intensity could be related to minor degradation of the material over time, suggested by the high rate of occurrence within the OH and CO groups, though this could not be isolated and tested for through this study and poses an opportunity for further exploration. The limited spectral range of the ZnSe/TE-MCT-A as currently equipped (ending at 650 cm⁻\u0026sup1;) is one of the most significant constraints, especially for materials that present diagnostic peaks in the lower region of the mid-IR spectrum, particularly inorganic additives or environmental contaminants, such as was the case for samples classed into Group 1. When standardized processing techniques are applied consistently across instruments, the reproducibility of data is enhanced but still presents some variability. This suggests that with appropriate processing, FTIR results are more transferable across platforms, supporting the broader comparability of adhesive residue analyses and facilitating collaborative, multi-institutional research while emphasizing the urgency of establishing standardized protocols for such comparisons and the importance of explicitly outlining spectral processing steps.\u003c/p\u003e\u003cp\u003eThe findings of the orientation reproducibility test underscore the significant influence of surface topography and compositional anisotropy on FTIR spectral stability, particularly when measurements are collected in reflectance mode. As presented here, the optical properties of a sample\u0026rsquo;s surface, particularly its micro-topography, refractive index variation, and alignment of constituents based on shifted orientation on the measurement platform, can introduce detectable variability in spectral outcomes, even when instrumental parameters are held constant. Interestingly, samples such as 91\u0026thinsp;\u0026minus;\u0026thinsp;26 (spruce resin with ochre) and 91\u0026thinsp;\u0026minus;\u0026thinsp;29 (spruce resin with sand) exhibited minimal spectral variation across orientations. In this case, these samples present visually even particulate coatings, resulting in relatively uniform reflection with limited dependence on beam angle even with the presence of mineral-based additives. The mineral additives in these samples, including finely ground ochre and fine sand, show largely isotropic and static behavior within the surrounding matrix, despite their crystalline structure, resulting in the stable scattering profiles and absorbance signals that are observed (Coats et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). The reflectance behavior of the crystalline additives, which, according to Izzo et al. (\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), is largely governed by surface roughness and refractive index variability, appears to be evenly distributed within the adhesive matrix across orientations in these cases, resulting in minimal variation between the spectra.\u003c/p\u003e\u003cp\u003eConversely, sample 91\u0026thinsp;\u0026minus;\u0026thinsp;11 (pine resin with flax fiber) showed substantial orientation sensitivity. Flax, a structured and fibrous organic inclusion, introduces anisotropic scattering due to its elongated morphology and directional grain of the filament. In reflectance FTIR, incident light interacts not only with the surface but also with microstructural angles (Coats et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Izzo et al. \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2020\u003c/span\u003e); as noted by Van Nimmen et al. (\u003cspan citationid=\"CR138\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) and Belbachir et al. (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), fibers aligned parallel to the beam may scatter differently or present different path lengths than those perpendicular to it. These interactions can lead to measurable reflectance shifts, which alter both spectral intensity and peak shape (Mercurio et al. \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Izzo et al. \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The resulting spectra showed poor correlation, high variability and large effect sizes clearly indicating that fiber orientation governs spectral outcomes in this case. Intermediate cases were observed in samples like 91\u0026thinsp;\u0026minus;\u0026thinsp;28 (spruce resin with ochre and clay), 91\u0026thinsp;\u0026minus;\u0026thinsp;30 (spruce resin with clay), and 91\u0026thinsp;\u0026minus;\u0026thinsp;32 (spruce resin with beef fat and clay), which demonstrated largely reproducible spectra with occasional shifts in baseline or peak sharpness. These minor deviations may stem from subtle surface layering or light trapping effects due to uneven spreading of crystalline additives within matrix films, which can introduce slight differences in reflectance interactions introduced by crystal orientation, (Mercurio et al. \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Izzo et al. \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Still, their overall reproducibility suggests that compositional homogeneity at the micro-scale may be sufficient to limit orientation-based variability.\u003c/p\u003e\u003cp\u003eProjecting orientation-shifted spectra into the PCA model provided insight into how both composition and microstructure can influence spectral classification. Spectra from 91\u0026thinsp;\u0026minus;\u0026thinsp;11, despite showing strong orientation effects in pairwise tests, clustered tightly in PCA space, suggesting that orientation-induced changes did not significantly affect the dominant chemical variance captured by the model. In contrast, samples 91\u0026thinsp;\u0026minus;\u0026thinsp;30 and 91\u0026thinsp;\u0026minus;\u0026thinsp;32 showed greater dispersion, likely reflecting compositional complexity and microstructural heterogeneity, such as uneven clay distribution or interactions between fat and mineral phases. Meanwhile, 91\u0026thinsp;\u0026minus;\u0026thinsp;28 and 91\u0026thinsp;\u0026minus;\u0026thinsp;29, which contain simpler or more homogeneously distributed additives, remained tightly grouped. These results demonstrate that PCA can capture variation introduced by both chemical composition and physical structure, factors that must be considered in archaeological applications where residues are often heterogeneous and degraded.\u003c/p\u003e\u003cp\u003eThese results highlight the importance of understanding how microstructural and topographic features influence IR reflectance, a factor that should not be overlooked in residue or artifact surface analyses. Spectral differences introduced by orientation are not necessarily indicative of chemical change, but rather of optical path length variation, scattering geometry, and local refractive behavior. For materials with directional structure, such as plant fibers, layered adhesives, or composite residues, careful sample mounting, surface flattening, or use of transmission or ATR modes may be required to ensure data accuracy and comparability. In reflectance-mode fieldwork or heritage contexts where control over sample presentation is limited, orientation effects should be explicitly tested and, if necessary, normalized or excluded from quantitative comparisons.\u003c/p\u003e\u003cp\u003eUltimately, these findings support that spectral reproducibility is not solely a function of instrument fidelity, but also of material surface behavior under IR illumination. Residue analysis protocols should therefore consider orientation tests as a part of methodological validation, particularly when dealing with organic or composite surfaces whose topography may distort reflectance behavior. To improve the reliability of reflectance FTIR-based residue interpretation, we recommend sampling a minimum of three to five distinct points on a single sample surface, where preservation permits. This approach helps capture intra-sample variability and reduces the risk of basing interpretations on localized contamination or spectral anomalies. It is also advisable to target macroscopically visible residues and adjacent areas to obtain a more comprehensive chemical profile, which can strengthen confidence in material identification. Additionally, sampling areas of the lithic substrate without the suspected residues present provides an important background reference, offering insights into the potential influence of the underlying material and any adhering sediments from burial. These background spectra are critical for distinguishing authentic residues from post-depositional contamination and for understanding how the substrate and burial conditions may affect the resulting spectra of perceived residues.\u003c/p\u003e\n\u003ch3\u003e4.b. Classification Challenges and Interpretive Nuances\u003c/h3\u003e\n\u003cp\u003eThis study demonstrates both the promise and limitations of FTIR spectroscopy for identifying organic adhesives in archaeological contexts, particularly when addressing composite materials or comparing data derived from reflectance-mode collection to existing transmission-mode standards. The successful alignment of major peaks between reflectance-mode spectra (following Kramers-Kronig correction) and transmission-mode spectra underscores the viability of reflectance-mode FTIR, particularly when destructive sampling is not permitted. However, the higher spectral noise observed in reflectance-mode spectra compared to transmission-mode is likely to be exacerbated in archaeological materials, presenting potential challenges in successful material identification. The possibility of minor shifts in peak position as a result of instrumental differences should also be considered, especially when analyzing archaeological materials which are subject to increased compositional variation as a result of degradation, taphonomic influence, diagenetic processes, and other possible points of contamination from environmental sources or from material handling.\u003c/p\u003e\u003cp\u003eVisual grouping of spectra by analysts proved effective for initial classification to distinguish adhesive types including gums, plant resins, tars, waxes, and animal-derived glues. However, visual interpretation is subject to analyst experience and requires extensive knowledge. To mitigate this, chemometric methods including PCA and HCA were tested. While PCA did accurately reflect visually defined clusters, particularly for more chemically distinct groups, clear trends were not always apparent without prior classification input. For example, in Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003e the spruce-based group and birch tar group are somewhat poorly separated in PCA space. This can be seen even more within the validation set when projected onto calibration data, such as in Fig.\u0026nbsp;\u003cspan refid=\"Fig15\" class=\"InternalRef\"\u003e15\u003c/span\u003e, where we see pure pinus samples overlapping with the spruce-based group and pure beeswax overlapping with the pinus-based group. These results suggest that PCA is useful for visualizing spectral trends in chemically distinct samples, but it remains limited in resolving more complex or overlapping composite mixtures.\u003c/p\u003e\u003cp\u003eMoreover, the PCA outcomes highlight a key challenge in spectral interpretation, being that instrumental artifacts influence variance and can obscure meaningful chemical differences, even with rigorous preprocessing. For the ZnSe/TE-MCT-A dataset, KKT-processed spectra captured relatively little variance in the first PCs, with a wide margin of variance still left in the residuals. By contrast, the raw data retained more variance in the modeled space and showed better agreement between calibration and validation, though the loadings remained relatively noisy. The KBr/LN-MCT-B dataset revealed the opposite pattern, with KKT preprocessing producing stronger separation and more stable distinctions when considering loadings, while the raw spectra were less consistent. When comparing the recipe-based and analyst-defined groupings, the underlying PCA space remains identical, yet the visual outcomes differ significantly: adhesive-defined classes highlight coherent, compositionally meaningful clusters, such as the clear separation between spruce- and pinus-based resins and the tight clustering of beeswax, birch tar, and protein-based adhesives. By contrast, analyst-driven groupings obscure these patterns in PCA space, merging spruce and pinus resins into partially overlapping clusters and scattering resin-wax mixtures across multiple groups.\u003c/p\u003e\u003cp\u003eThese contrasts emphasize that interpretive outcomes are shaped as much by classification frameworks as by instrument platform, preprocessing choices, and chemistry itself, underscoring the importance of transparency when presenting PCA-based residue studies. While each dataset shows internal structure and reflects meaningful patterning, the variance captured is fragile, and much of the signal remains difficult to interpret. This outcome suggests that although each instrument captures meaningful group differentiation, the nature of that differentiation is not directly comparable between platforms. While intra-instrument PCA models are reliable for exploratory analysis, inter-instrument comparability remains limited without advanced harmonization strategies. The models used here do capture real signal variation, exhibited by the group structures observable in each case, which provides a defensible foundation for class discrimination, outlier detection, and future supervised modeling. These results emphasize the importance of transparency in preprocessing documentation and reporting, robust cross-validation, and cautious interpretation when integrating chemometric results across platforms. These differences highlight the need for instrument-specific modeling strategies, as models optimized for one platform likely will not translate effectively to another due to differences in detector sensitivity, spectral resolution, and reflectance behavior. For robust classification and cross-instrument comparability, it is essential to tailor preprocessing, model construction, and validation procedures to each system\u0026rsquo;s signal properties. These findings collectively reinforce the methodological importance of both platform-aware preprocessing and model customization, particularly in archaeological FTIR applications where reproducibility and interpretive accuracy are critical.\u003c/p\u003e\u003cp\u003eThe clustering outcomes from HCA accentuates the critical influence of both preprocessing strategy and dimensionality reduction on the resolution and reliability of spectroscopic classifications. Performing HCA directly on preprocessed spectral data retained fine-scale spectral variation but often resulted in over-clustering or fragmentation of chemically similar adhesives, likely due to the high dimensionality and residual noise in the dataset. By contrast, integrating PCA prior to clustering helped streamline the data structure, concentrating on the most informative variance while suppressing minor fluctuations that do not contribute meaningfully to chemical differentiation. The choice of three to five principal components was particularly effective, as it captured the majority of chemically relevant variance without incorporating noise-dominated components. The improved clustering performance in the reduced and subsampled datasets also suggests that redundancy in spectral replicates can obscure class boundaries, emphasizing the value of representative sampling in spectroscopic studies.\u003c/p\u003e\u003cp\u003eThese findings highlight the importance of tailoring multivariate approaches to the specific characteristics of the dataset and instrumentation. For archaeological residue analysis, where samples are often limited and compositionally complex, combining robust preprocessing, dimensionality reduction via PCA, and thoughtful clustering algorithms like Ward\u0026rsquo;s method can substantially improve the interpretability and accuracy of material classification. This integrated approach also supports the development of scalable analytical pipelines adaptable to larger and more variable archaeological datasets.\u003c/p\u003e\u003cp\u003eThe outcomes of the validation test complicate our understanding of how PCA-based models function in residue classification. While the models do capture meaningful patterns in spectral data, the misalignment of pure validation samples, particularly those that cluster unexpectedly or ambiguously, highlight both the strengths and the limitations of this approach. That even well-preserved, compositionally simple substances such as pure resins and waxes do not consistently align with their expected adhesive classes reveal the extent to which PCA may be influenced by the specific makeup of the calibration dataset. In this case, the training model seems to have captured overlapping spectral signals arising from shared ingredients rather than cleanly distinguishing each material based on its dominant component. This effect is particularly evident in the clustering of pinus resin within the spruce-based cluster and the proximity of beeswax to our pinus-based group. This may indicate that PCA is organizing the samples based on global spectral similarity rather than isolating distinct chemical signatures. The relatively tight clustering of the mixed adhesives compared to the pure materials further supports this interpretation, indicating that the PCA model may be better tuned to capturing composite patterns than identifying discrete differences in source materials. Inspection of the loadings reinforces this view \u0026ndash; even in the leading components, the profiles appear noisy, with more subtle variation confined to later components or residuals. In the ZnSe/TE-MCT-A data, this resulted in less stable grouping under KKT preprocessing and better performance on raw spectra, while the KBr/LN-MCT-B showed the opposite trend. In the score plots, adhesive-defined classes in the training set separate more clearly than analyst-defined classes; proteins, birch tar, and beeswax form compact clusters, while spruce and pinus resins, though distinct in the training set, overlap when tested on validation data. This pattern is consistent with the loadings, where diagnostic resin-specific C-O stretching (~\u0026thinsp;1100\u0026thinsp;\u0026minus;\u0026thinsp;1000 cm⁻\u0026sup1;) and beeswax CH₂ deformation (~\u0026thinsp;1450 cm⁻\u0026sup1;) are emphasized in later components (PC4-PC5) but underweighted in PC1-PC2, which instead capture broad overlapping features that dominate variance across both pure substances and mixtures. Together, these results show that mixtures may cluster tightly because their broad variance patterns align with the dominant PCs, while pure substances misalign when their distinctive features fall into later components. Just as importantly, they demonstrate that outcomes differ systematically between instruments, meaning preprocessing strategies cannot be assumed to transfer uniformly across platforms, further stressing the importance of transparent reporting practices.\u003c/p\u003e\u003cp\u003eThese findings have significant implications for archaeological applications. Residues recovered from artifacts are rarely pure and often heavily degraded, mixed, or contaminated, making them significantly more complex than the validation samples used here. If the model cannot clearly resolve known, pristine substances, it will likely be challenged in detecting definitive differences in archaeological adhesive recipes. Instead, the PCA model should be seen as a tool for exploratory pattern recognition, helping to identify samples with broadly similar compositions, suggesting potential strategies for further investigation, and flagging samples of potential interest for more targeted or even destructive analyses. Visual inspection of spectra remains critical and will offer clearer insights into spectral similarity than multivariate analysis alone. To strengthen archaeological applicability, future work must expand experimental reference datasets, incorporate experimentally degraded or mixed adhesives, and develop multimodal analytical protocols that integrate FTIR with complementary techniques such as use-wear analyses and GC-MS. Moreover, rethinking the design of calibration datasets to better reflect the types of residues we expect to encounter in archaeological contexts will help ensure that PCA or HCA models offer meaningful and realistic interpretive scaffolds. Without these advancements, the current model risks oversimplifying the interpretive space of archaeological residues, potentially obscuring rather than clarifying the technological behaviors we seek to reconstruct.\u003c/p\u003e\u003cp\u003eSubstrate interference remains a concern in both spectral data collection and interpretation, often introducing noise or misrepresenting spectral features. In such cases, fingerprint regions can be variably impacted by substrate, particularly when residue deposits are thin or cover a small area, emphasizing the need for controlled background subtraction and the inclusion of blank lithic spectra in future archaeological reference libraries. While residue thickness, opacity, and roughness can generally be assessed visually under a microscope, such observations rarely eliminate the problem. Residues are often preserved in such small quantities that even with a very small aperture (e.g., 100 \u0026times; 100 \u0026micro;m), it is not possible to fully isolate them from the underlying lithic or associated sediments when collecting FTIR measurements. In addition, surface topography exerts a strong influence on spectral quality, as archaeological residues are rarely flat and uneven surfaces can distort absorbance intensity or introduce scattering effects. As a result, interfering signals should be expected, whether or not residues appear opaque under magnification. Such interferences, compounded by degradation processes over time, highlight the limitations of comparing ancient samples directly with fresh modern references. To mitigate some of these challenges, multiple spectra per sample should be collected from different points across the residue deposit, which would require a significant increase in sample and scan numbers and may not always be feasible.\u003c/p\u003e\u003cp\u003eDespite these challenges, the study\u0026rsquo;s visual classification strategy revealed meaningful compositional groupings, reflecting variation in base adhesives and additives. Several samples presented as outliers or transitional types, illustrating the substantial spectral impact of additives and preparation techniques, such as thermal processing. Specimens containing similar base materials were sometimes grouped differently due to their unique additive profiles or signs of repeated heating, which may chemically alter their spectral signatures. While the experimental mixtures reflect a broader range of material types and combinations than typically expected in a single archaeological context, the diversity was intentional to test the limits of the attempted classification methods under complex and potentially overlapping conditions. As such, some of the complications encountered within this study may not be as extreme when applied to true archaeological assemblages. Regardless, these nuances are important in archaeological interpretation, where understanding the technological choices of past people, including mixture recipes or heat treatment strategies, relies on accurate residue classification. The classification scheme explored here provides a framework for characterizing complex adhesive formulations, with each group reflecting distinct chemical profiles, while overlapping features in some samples underscore the inherent fluidity of real-world adhesive technologies.\u003c/p\u003e\n\u003ch3\u003e4.d. Archaeological Applications and Interpretive Value\u003c/h3\u003e\n\u003cp\u003eReflectance-mode FTIR serves as a valuable non-destructive step within an integrated approach to identifying glues on archaeological tools by first detecting the presence of organic residues and then characterizing their molecular composition based on diagnostic absorption bands. Adhesives such as birch tar, conifer resins, and plant gums exhibit unique spectral features that help distinguish them from other plant-derived residues. To move from detection to confident identification, these spectral signatures must be compared against well-curated experimental reference datasets that include both fresh and degraded forms of known adhesives. However, interpreting these results reliably also requires ruling out alternative sources of similar compounds, such as naturally occurring plant exudates encountered during woodworking or incidental contact with resinous woods as well as contextual data that relates the residues to tool function.\u003c/p\u003e\u003cp\u003eBecause spectral similarities can exist between some adhesive types and incidental residues introduced through other means, a multi-proxy approach is essential for strengthening interpretations. Use-wear analysis can provide independent evidence of how a tool was used, revealing patterns of wear consistent with hafting or adhesive application. Functional analyses can further clarify whether residues align with expected patterns from tasks like cutting, scraping, or drilling. When combined, chemical data from FTIR and morphological evidence from use-wear studies provide a more robust framework for interpreting the presence and function of adhesives in archaeological contexts. This integrated strategy mitigates the limitations of any single method and helps ensure that interpretations of prehistoric adhesive use are both chemically and behaviorally grounded.\u003c/p\u003e\u003cp\u003eThe methodological approaches presented in this study offer significant, though cautioned, potential for application to archaeological materials. The ability to generate reproducible and interpretable FTIR reflectance spectra across different instruments strengthens the reliability of non-destructive residue interpretation, which is a critical step toward integrating FTIR as a standard in sampling of archaeological residues where destructive techniques such as GC-MS are restricted due to preservation ethics or limited sample availability.\u003c/p\u003e\u003cp\u003eThe experimental adhesives analyzed here were selected to simulate a broad range of materials found in prehistoric hafting technologies The groupings and classification criteria developed provide a framework for interpreting similarly complex residues found on archaeological tools, offering preliminary reference signatures that may be useful for identifying material classes even when exact compositional matches are unavailable. Importantly, the study highlights how variations in adhesive composition, such as the inclusion of fat, ochre, or thermally altered additives, can significantly alter FTIR spectral profiles which can reflect meaningful differences in technological practice. The spectral distinctions identified here between different mixtures and processing techniques (such as repeated heating) could be used archaeologically to infer variability in craft traditions, standardization of recipes, or regional knowledge transfer.\u003c/p\u003e\u003cp\u003eBeyond individual spectra, the comparative modeling and classification techniques explored here, particularly PCA and visual groupings, offer scalable strategies for handling archaeological datasets. Although the validation set employed in this study consists of experimentally controlled replicates, future work may substitute this component with spectra obtained from archaeological sources. In such cases, the experimentally derived adhesive references would continue to serve as the training set, providing a structured baseline against which unknown archaeological residues can be assessed. While the inherent complexity and degradation of archaeological materials may reduce classification confidence, the defined groupings and chemometric thresholds developed here can remain a useful guide for interpreting patterns of spectral similarity and divergence. Additionally, this framework provides a step toward refining machine-learning approaches to residue classification as larger and more diverse archaeological datasets become available.\u003c/p\u003e\u003cp\u003eFurther consideration concerns the visibility and thickness of residues on archaeological tools. While fully transparent adhesives like acacia gum are not common in the archaeological record, thin, minute, degraded, or weathered residue layers are frequently encountered. The spectral challenges presented by transparent substances in this study, especially in distinguishing their signals from lithic substrates, thus share important overlap with real-world archaeological conditions. Like transparent experimental residues, small archaeological deposits often exhibit low signal intensity, high noise, and substrate-dominated spectra. These similarities suggest that the methodological strategies outlined here, particularly the use of multiple sampling points, careful background subtraction, and substrate-matched reference spectra, are directly transferable to archaeological contexts. In this sense, even if exact material matches are not always available, the experimental findings remain crucial for informing data quality assessment, error margins, and interpretive caution in archaeological FTIR studies.\u003c/p\u003e\u003cp\u003eThis study also underscores several caveats essential to archaeological interpretation. Spectral quality of a residue is influenced by thickness of the deposit, transparency, and the reflectance properties of the underlying lithic substrate, residue surface, and inclusions within the residue itself. These conditions, common in archaeological contexts, can cause substantial interference, skewing spectral results and complicating interpretation. Additionally, while visual classification by analysts remains a powerful tool, its subjectivity poses a limitation in archaeological cases where relevant reference data are incomplete or inaccessible. The use of chemometric approaches such as PCA and cluster analysis, while still limited in resolving all material classes, may offer a somewhat standardized supplement to visual classification in detecting patterns within assemblages. These methods provide a potential route for handling large datasets or blind testing of unknown archaeological residues, especially in collaborative or multi-institutional research settings.\u003c/p\u003e\u003cp\u003eLooking forward, the reproducibility demonstrated here between instruments, and the clarity of material groupings among experimental datasets, suggest several promising avenues for archaeological application. One such avenue is to serve as a screening method to prioritize samples for destructive analysis like GC-MS. Taking the materials here as a case study, several samples could reasonably be excluded from further analysis based on near exact reference matches (such as pure beeswax and pure acacia gum), prioritizing more compositionally complex samples such as birch tar and tree resin-based mixtures. Extending the example further, if destructive analysis were permitted on only three samples from this set, we might select samples 91\u0026thinsp;\u0026minus;\u0026thinsp;07 (birch tar), 91\u0026thinsp;\u0026minus;\u0026thinsp;23 (pine resin and fat mixture), and 91\u0026thinsp;\u0026minus;\u0026thinsp;27 (spruce resin, ochre and sand mixture) based on the outcome of the visual classification and comparison with reference spectra combined with PCA and HCA. These represent cases with ambiguous or absent reference matches where compositional overlap complicates classification and where complementary chemical analysis would most improve interpretive certainty. Other potential applications include classification of unknown archaeological residues into generalized material classes (resinous, proteinaceous, waxy, etc.) to support behavioral interpretation, assessment of adhesive degradation by comparing archaeological spectra to modern reference sets as well as simulated taphonomic alteration, and identification of technological variability across space and time, such as shifts in adhesive recipes or the introduction of new ingredients, which may reflect cultural transmission or environmental adaptation.\u003c/p\u003e\u003cp\u003eEnsuring data availability is essential for advancing residue studies in archaeological science, particularly when addressing questions of reproducibility and methodological consistency. By making both raw and processed FTIR spectra, along with associated metadata, openly accessible, we enable other researchers to critically evaluate, replicate, or extend our findings. This transparency not only fosters scientific rigor but also facilitates further cross-instrument and cross-laboratory comparisons, which are necessary for establishing broader analytical standards in residue detection and advancing the progression of reflectance FTIR in archaeological applications. In the context of ongoing debates surrounding the reliability of biochemical residue identification, open data serves as a critical resource for improving methods and refining interpretations within the field.\u003c/p\u003e\u003cp\u003eBy bridging experimental and archaeological research, this study contributes not only a base-level methodological toolkit for improving FTIR reproducibility, but also a conceptual framework for interpreting adhesive technologies in archaeological contexts. Through careful calibration, comparison, and classification, FTIR residue analysis has the potential to shed light on the cognitive, technological, and cultural dimensions of prehistoric tool manufacture when strategically applied.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThe results presented here affirm that reflectance-mode FTIR, particularly when paired with strong reference libraries and thoughtful statistical approaches, can be a powerful tool for identifying unknown and potentially ancient adhesives. This study demonstrated improved spectral comparability across KBr/LN-MCT-B and ZnSe/TE-MCT-A platforms through standardized processing techniques, though gains in alignment came with some distortion of spectral shape, emphasizing the need to consider the effects of spectral processing on overarching goals, as well as the need to balance quantitative normalization with the preservation of diagnostic features. In addition to instrument variability, analyst decisions, classification strategies, and preprocessing choices were shown to influence interpretive outcomes. Visual classification and chemometric modeling each contributed valuable insights, but both require transparent criteria and representative training data to ensure consistent, transferable results. Chemometric tools such as PCA and HCA proved useful for visualizing group cohesion and assessing the internal consistency of classifications, especially in datasets with overlapping spectral features. These findings emphasize the importance of reproducible workflows and reporting practices to enhance comparability and collaborative potential across laboratories. It also reinforces the broader viability of reflectance FTIR as a transferable, reproducible tool for archaeological residue analysis, particularly when destructive sampling is limited or prohibited. Ultimately, this study supports a multi-method approach to adhesive residue analysis. While reflectance-mode FTIR provides rapid, non-destructive compositional data, expert interpretation, visual inspection and complementary analytical techniques remain essential, but each come with a suite of challenges. Continued expansion of reference libraries, along with further testing of degradation pathways and substrate interactions and development of robust, transferable methodologies, will enhance the accuracy, consistency and interpretive reliability of FTIR-based residue analysis in archaeological research.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eDeclarations\u003c/h2\u003e\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAll authors contributed to the study design. Data collection and analysis were performed by L.L. and S.M. Figures were produced collaboratively by L.L. and S.M. The first draft of the manuscript was written by L.L., and all authors commented on previous versions. All authors read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe funding for the purchase of the Agilent KBr/LN-MCT-B system was provided by a Deutsche Forschungsgemeinschaft (DFG) grant to Christopher Miller (MI 1748/1\u0026ndash;1). The license for the PLS Toolbox software is hosted in the University of T\u0026uuml;bingen with funding for its annual maintenance provided by the Leibniz Association via the Geogenomic Archaeology Campus T\u0026uuml;bingen (GACT). The funding for the purchase of the Bruker LUMOS II ZnSe/TE-MCT-A system was provided by a F.R.S.-FNRS equipment grant to Veerle Rots. The license for the Peak Spectroscopy software is supported by the GLUE project funded by the University of Li\u0026egrave;ge (Collaborative Research Actions \u0026ndash; ARC). All experimental materials were produced at the University of Li\u0026egrave;ge by Christian Lepers, experienced primitive technologist. Veerle Rots is indebted to the FNRS (RD), Lauren Lien to the University of Li\u0026egrave;ge (ARC).\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets generated and analyzed within this study are available in the ULi\u0026egrave;ge Dataverse repository at https://doi.org/10.58119/ULG/BVGFPW. Naming conventions for each dataset are described in Supplementary Information, Table 1 (ST1). R scripts used for data processing and analysis are openly available on GitHub at https://doi.org/10.5281/zenodo.17160767\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAdam, H., Siddig, M. A., Siddig, A. A., \u0026amp; Awad Eltahir, N. (2013). 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A review of plant residue analysis on knapped lithic artifacts. \u003cem\u003eLithic Technology\u003c/em\u003e, \u003cem\u003e49\u003c/em\u003e(1), 63\u0026ndash;80. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://DOI:10.1080/01977261.2023.2188343\u003c/span\u003e\u003cspan address=\"https://DOI:10.1080/01977261.2023.2188343\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"FTIR, residue, adhesive, lithics, spectroscopy, experimental archaeology","lastPublishedDoi":"10.21203/rs.3.rs-7711776/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7711776/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eReflectance-mode Fourier transform infrared (FTIR) spectroscopy is increasingly employed in archaeological residue studies, offering a non-destructive means to investigate Paleolithic adhesive technologies. This study evaluates the reproducibility and comparability of reflectance-mode FTIR spectra collected from experimental adhesives on flint substrates, analyzed across an eight-year interval using two different FTIR instruments. A comprehensive suite of natural resins, gums, glues, and admixtures was assessed to examine spectral variability introduced by instrument configuration, sample orientation, and residue composition. To evaluate classification accuracy and interpretive consistency, both analyst-defined and ingredient-defined grouping strategies were applied to processed spectra. Chemometric methods including Principal Component Analysis (PCA) and Hierarchical Clustering Analysis (HCA) were used to investigate compositional trends and clustering, supplemented by a blind validation set of pure adhesives. While key chemical features were preserved across instruments after standardized processing, minor spectral differences introduced variability in chemometric clustering. In contrast, analyst-based groupings following a Kramers-Kronig transformation remained largely consistent across instruments and sample conditions. The results highlight the value of integrating visual inspection with chemometric tools and underscore the importance of tailored preprocessing strategies, transparent classification criteria and realistic experimental references. Reflectance-mode FTIR, when paired with reproducible workflows and robust interpretive strategies, offers a promising approach for identifying archaeological adhesive residues, particularly in contexts where destructive sampling is limited.\u003c/p\u003e","manuscriptTitle":"FTIR Analysis of Experimental Adhesives: Investigating Spectral Reproducibility, Chemometric Approaches, and Archaeological Applications","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-01 07:31:40","doi":"10.21203/rs.3.rs-7711776/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"74ce7584-c324-4acd-8769-9d569ee8c880","owner":[],"postedDate":"October 1st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-10-02T23:08:12+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-01 07:31:40","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7711776","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7711776","identity":"rs-7711776","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}