Harmonizing infrared spectroscopic techniques for microplastic identification: a comparative evaluation of ATR and µFTIR transmission and reflection modes

Harmonizing infrared spectroscopic techniques for microplastic identification: a comparative evaluation of ATR and µFTIR transmission and reflection modes | 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 Harmonizing infrared spectroscopic techniques for microplastic identification: a comparative evaluation of ATR and µFTIR transmission and reflection modes Barbara Geraldino, Stella Melgaço, Daniele Maia Bila This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6683637/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 This study systematically evaluated the performance of Attenuated Total Reflectance (ATR) and micro-Fourier Transform Infrared Spectroscopy (µFTIR) in transmission and reflection modes for the identification of microplastics (MPs) from real-world plastic products. Twelve common polymers were analyzed in two particle size ranges (500 µm and 100 µm). Identification quality was assessed using the Hit Quality Index (HQI), with a validation threshold of ≥ 70%. ATR analysis of 500 µm particles yielded HQI values > 80% for all polymers, with PET (97.2%), ABS (93.2%), and EVA (92.3%) achieving > 90%, demonstrating high spectral fidelity and reproducibility. µFTIR exhibited significant size-dependent variation: for 100 µm particles, reflection mode (R100) achieved HQI values > 85% for most polymers, including 94.5% for PS and 93.7% for epoxy resin. Conversely, µFTIR performance declined for 500 µm particles, with HQI values < 70% for PET, PS, epoxy resin, and PP. ANOVA (p < 0.0001) and Tukey’s post hoc test confirmed significant differences across techniques and particle sizes, with R100 performing comparably to ATR. These results highlight the influence of particle morphology and acquisition mode on spectral identification and emphasize the need for harmonized analytical protocols. These findings contribute empirical support for the refinement of current standards (e.g., ASTM D8333-20, ISO 24187:2023) recommending HQI ≥ 80% as a reliable threshold for polymer identification via µFTIR. Microplastics ATR-FTIR µFTIR Hit Quality Index Polymer identification Environmental monitoring Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction The presence of microplastics (MPs) has been increasingly reported across a wide range of environmental matrices—from deep-sea sediments to remote locations such as Antarctic snow (Jones-Williams et al. 2025 ). Global surveys have identified MPs in marine environments (Mutuku et al. 2024 ; Pegado et al. 2024 ), surface waters (Hoehn et al. 2024 ; Trindade et al. 2023 ), air (Ortega and Cortés-Arriagada 2023 ; Ferraz et al. 2024 ), soil (Yang et al. 2022; Sajjad et al. 2022 ), freshwater lakes (Dong et al. 2025 ; Chen et al. 2024 ), food items (Kumar et al. 2024 ; Alberghini et al. 2022 ), bottled water (Al-Mansoori et al. 2025 ; Weisser et al. 2021; Li et al. 2023 ), salt (Di Fiore et al. 2023 ), aquatic organisms (Nodehi et al. 2024 ; Liu et al. 2025 ), and even in human tissues—including the brain, atheromas, and placenta (Nihart et al. 2025 ; Amato-Lourenço et al. 2024 ; Ragusa et al. 2021 ; Sharma et al. 2024 ; Marfella et al. 2024 ). Reported concentrations range from as low as 2.5 particles per cubic meter in ocean surface waters (Cózar et al. 2014 ) to over 8,500 particles per liter in urban runoff and sewer overflow events (Sun et al. 2023 ), highlighting the pervasive and heterogeneous nature of microplastic pollution. Accurate detection and quantification of MPs and nanoplastics remain analytically challenging due to the complexity of environmental matrices and the physicochemical diversity of the particles (Rani et al. 2023 ; Jiménez-Lamana et al. 2023 ; Huang et al. 2023). Their small size, morphological heterogeneity, and surface contamination complicate differentiation from natural materials and reduce identification accuracy (Jia et al. 2024 ; Fu et al. 2021 ). Biofilm formation and adsorption of organic matter may obscure spectral features, while pigments and additives can interfere with polymer-specific signals. Sampling methods can also introduce biases: Lindeque et al. ( 2020 ) showed that 100 µm mesh nets captured 2.5 times more MPs than 333 µm nets and 10 times more than 500 µm nets, demonstrating that smaller particles—arguably the most relevant—are often underrepresented in environmental monitoring protocols (Sharma et al. 2023 ). Optical microscopy remains commonly used for initial screening based on shape, color, and size (He et al. 2018 ; Kumar et al. 2020 ; Barceló et al. 2023 ). However, it is time-consuming, subjective, and highly error-prone, particularly for small or transparent particles. Misidentification rates exceeding 70% have been reported, especially involving cellulose fibers, organic debris, and mineral fragments (Shim et al. 2017 ). Fourier-transform infrared (FTIR) spectroscopy is one of the most widely used techniques for MP identification, offering non-destructive chemical characterization with high specificity. Analyses can be conducted in Attenuated Total Reflectance (ATR) mode—which requires direct contact with the ATR crystal—or via micro-FTIR (µFTIR), which integrates infrared detection with optical microscopy. µFTIR can operate in either transmission or reflection mode, depending on how the IR beam interacts with the sample and its substrate (Maurizi et al. 2023 ). Each acquisition mode exhibits different sensitivities to variables such as thickness, morphology, pigmentation, and degradation. Polymer identification relies on spectral matching against reference libraries (Primpke et al. 2020 ), but spectral quality may vary depending on acquisition mode and sample features. Many studies use a single acquisition mode without evaluating how polymer morphology, surface characteristics, or weathering impact spectral fidelity. This limits cross-study reproducibility and hinders progress toward harmonized monitoring protocols. Moreover, current standards—such as ASTM D8333-20 and ISO 24187:2023—acknowledge the role of sample morphology but lack quantitative guidance on particle size limits or spectral quality benchmarks, such as the Hit Quality Index (HQI). To address these limitations, the present study offers a systematic evaluation of the analytical performance of ATR and µFTIR—operated in both transmission and reflection modes—on twelve polymers commonly found in consumer products. Fragmented particles of standardized sizes were analyzed under consistent experimental conditions, including identical spectrometer settings and validation criteria based on HQI and expert visual inspection. By investigating how particle size and spectroscopic mode affect spectral fidelity and polymer classification, this study contributes to ongoing efforts toward methodological harmonization and supports the development of robust protocols for identifying environmentally relevant microplastics. These insights are particularly valuable for improving the reliability of environmental monitoring programs and interlaboratory comparability. 2. Materials and Methods 2.1. Sample preparation Twelve polymers were selected to represent a wide range of plastic materials commonly encountered in consumer products and environmental samples. These included polyethylene (PE), polystyrene (PS), polypropylene (PP), acrylonitrile butadiene styrene (ABS), polyethylene terephthalate (PET), ethylene-vinyl acetate (EVA), nylon, polycarbonate (PC), polyurethane (PU), polyvinyl chloride (PVC), epoxy resin, and polymethyl methacrylate (PMMA). The diversity in chemical composition among these materials reflects the typical heterogeneity found in environmental microplastic pollution. All polymers were sourced from commercially available household items, with product type, manufacturer, and brand recorded for traceability. Each product was inspected to ensure the absence of surface coatings, composite structures, or multilayered elements prior to sample processing. Plastic fragments were manually cut using stainless-steel scissors cleaned with ethanol and deionized water to prevent cross-contamination. Edges were standardized by sanding with fine-grit abrasive paper, both to simulate mechanical weathering and to reduce heterogeneity in thickness and surface roughness. Fragments were rinsed three times with ultrapure deionized water and stored in sterile glass Petri dishes until analysis. Preliminary visual inspection was performed under a stereomicroscope (10× to 20× magnification) in a clean workspace to ensure the absence of visible contaminants or extraneous particles. All tools and containers were pre-cleaned and rinsed three times with ultrapure water. Procedural blanks were maintained throughout sample preparation to monitor potential contamination. Fragments approximately 500 µm and 100 µm in size were selected using a calibrated scale affixed to the microscope stage. Individual particles were transferred using ethanol-cleaned, antistatic titanium tweezers and stored in sealed, pre-cleaned glass vials to avoid particle loss or contamination before spectroscopic analysis. An overview of the methodological approach used for microplastic identification is shown in Fig. 1 , which illustrates the main analytical procedures, the types of polymers assessed, the particle size ranges, and the spectral acquisition modes compared (ATR-FTIR and µFTIR in transmission and reflection). 2.1. Analytical Techniques 2.1.1.ATR Initial polymer characterization was conducted using Attenuated Total Reflectance Fourier-Transform Infrared (ATR-FTIR) spectroscopy (IRTracer-100, Shimadzu). Twelve 500 µm particles (one per polymer) were analyzed using a diamond ATR crystal. Each spectrum was acquired using 45 scans, over the 400–4000 cm⁻¹ range, at 4 cm⁻¹ resolution. Instrument control and data processing were performed using LabSolutions IR software. Baseline correction was applied using the built-in algorithm, which fits a curve through spectral minima to reduce background deviations. The Happ-Genzel apodization function was used, as recommended for spectroscopic analysis of heterogeneous materials (Andrade et al. 2020 ; Willans et al. 2023 ). 2.1.2. µFTIR- Transmission and Reflection Modes To compare spectral performance across acquisition modes, the same set of polymers was analyzed via micro-FTIR (µFTIR) using both transmission and reflection modes. Particles of ~ 500 µm and 100 µm were placed on zinc selenide (ZnSe) windows, selected for their high infrared transparency and minimal spectral interference. Measurements were performed using a Shimadzu AIM-9000 system, equipped with a liquid nitrogen-cooled mercury cadmium telluride (MCT) detector. Instrument parameters matched those used in ATR-FTIR analysis. Background spectra were collected separately for each acquisition mode to minimize environmental interferences (Andrade et al. 2020 ; Willans et al. 2023 ). The aperture size was set to 25 × 25 µm. Spectral post-processing involved baseline correction and subtraction of atmospheric CO₂ bands, using AIMsolution software. All settings were standardized to maintain comparability across techniques and support harmonized reporting practices in microplastic FTIR analysis (Munno et al. 2020; Kozloski et al. 2024 ). 2.2. Evaluation of Results Consistency To ensure analytical reliability, each sample was measured in triplicate under identical conditions. Spectra were accepted only if the variation in Hit Quality Index (HQI) across replicates remained below 10%, serving as a quality control threshold aligned with best practices in polymer spectral analysis (Andrade et al. 2020 ). Spectral identification was performed by matching measured spectra against three reference libraries (ATR-Polymer2, IRs-Polymer2, and T-Polymer2). A minimum HQI of 80% (800/1000) was adopted as the acceptance threshold, based on prior studies (Primpke et al. 2020 ; Clough et al. 2024 ). While ASTM D8333-20 does not mandate a fixed HQI value, the ≥ 80% cutoff has been validated as a reliable indicator of spectral match quality in recent literature. Additional performance verification was conducted using standardized polyethylene microspheres (63–75 µm, Cospheric, Santa Barbara, CA, USA), which served as internal controls to assess system stability and spectral reproducibility (ASTM, 2020 ). Statistical analysis was performed using SPSS version 20. HQI values were compared across acquisition modes using one-way ANOVA, followed by Tukey’s post hoc test for multiple comparisons. The coefficient of variation (CV) was also calculated to evaluate intra-group variability. A significance threshold of p < 0.05 was adopted for all statistical tests. 3. Results and Discussion 3.1. Comparison between spectroscopic modes 3.1.1. Performance of the ATR Technique ATR-FTIR, applied in this study to analyze plastic particles ≥ 500 µm, demonstrated robust and consistent performance in identifying all twelve evaluated polymers. Each material achieved HQI values above 80% (Fig. 2 ), indicating excellent agreement with reference spectral libraries (Cowger et al. 2020 ; Gouda et al. 2025). Although ASTM D8333-20 is widely used as a reference for microplastic spectral analysis via µFTIR, it does not specify a fixed HQI threshold as a universal acceptance criterion. Instead, it emphasizes manual validation by experienced analysts, particularly for ambiguous spectra or potential interferences. In practice, however, HQI values ≥ 80% are commonly considered reliable for automatic acceptance, provided the polymer’s characteristic bands are confirmed. Scores between 70% and 79% generally require manual inspection of key bands, while values below 70% are typically rejected or require complementary techniques such as Raman spectroscopy or microscopy (Whiting et al. 2022). Notably, PET (97.2%), ABS (93.2%), EVA (92.3%), PP (90.5%), and PVC (90.2%) achieved HQI values exceeding 90%, with replicate variation 80% for fragments derived from everyday plastic products. Similarly, Whiting et al. (2022) validated HQI ≥ 70% as an acceptance criterion for automated FTIR identification, achieving up to 88.3% accuracy—reinforcing ATR’s suitability for reliable laboratory screening of microplastics. Polymers with slightly lower performance, such as PU (80%), PE (82.5%), PC (83.1%), and nylon (87.8%), still remained within the acceptable range. Meanwhile, PS (84.8%), PMMA (85.2%), and epoxy resin (85.3%) also showed satisfactory HQIs, although their spectral analyses may be more sensitive to morphological and physicochemical factors—including thickness, pigmentation, degradation, and surface roughness—previously reported to affect spectral quality (Käppler et al. 2016 ; Cowger et al. 2020 ; Invernizzi et al. 2022 ). Spectral quality in ATR is directly dependent on effective coupling between the sample and the ATR crystal. According to Cowger et al. ( 2020 ), thick or opaque samples improve optical coupling and tend to yield better-defined spectra. Conversely, rough, deformed, or irregular surfaces hinder uniform contact with the crystal, increasing infrared scattering and reducing spectral resolution (Invernizzi et al. 2022 ; Diaz & Thériault 2018 ). Inadequate spectral preprocessing can also amplify artifacts that interfere with automated identification. As Willans et al. ( 2023 ) noted, samples with irregular surface topography often produce distorted bands, peak shifts, and baseline variations due to diffuse reflection, compromising spectral fidelity. Therefore, strict quality control and spectral correction procedures are recommended, particularly for environmental analyses. Importantly, results with synthetic particles were consistent with preliminary findings from heterogeneous environmental samples, supporting ATR-FTIR’s potential—when thickness, cleanliness, and contact are properly controlled—for identifying microplastics in real-world scenarios. 3.1.2. Performance of the µFTIR Technique The application of µFTIR in this study revealed significant differences in performance between reflection and transmission modes, particularly as a function of particle size. For 100 µm fragments, reflection mode yielded slightly higher HQI values—often exceeding 85%—indicating high spectral fidelity. In contrast, both modes showed a marked decline in performance when analyzing 500 µm particles, with HQIs dropping below 70% for several polymers. Recent studies support these findings. Sefiloglu et al. (2024) and Silva et al. ( 2020 ) highlighted µFTIR’s high sensitivity for small particles, while Ivleva ( 2021 ) emphasized the need for integrated analytical approaches to address morphological complexity and degradation in environmental microplastics. Rathore et al. ( 2023 ) also demonstrated the effectiveness of reflection mode for particles ≤ 100 µm, reporting HQI values above 70% for polymers like PE and PET. In this study, ZnSe slides were used as optical substrates to enhance mid-infrared transmission (Liu et al. 2023; Emir et al. 2025 ), yet performance remained lower than reflection mode for small particles. Despite reflection mode’s relatively strong performance for small particles, certain limitations must be considered. The overlap of specular and diffuse reflection components can distort spectra, affecting band shape, intensity, and position (Cowger et al. 2020 ; Invernizzi et al. 2022 ). Spectral performance is closely tied to the sample’s physical and optical properties: thick or opaque particles are better suited to ATR, while thin and translucent fragments typically perform better in µFTIR, especially in transmission mode. Irregular topography—such as rough or deformed surfaces—can also reduce spectral resolution, as shown by Diaz and Thériault ( 2018 ). For complex structures like multilayered materials (e.g., CD-grade polycarbonate), ASTM D8333-20 specifically recommends µFTIR reflection mode due to its ability to accommodate morphological overlap and variation (Huber et al. 2020; ASTM 2020 ). 3.1.3. Comparative performance and reproducibility Statistical analysis confirmed that µFTIR performance is significantly affected by particle size. For 500 µm particles, both reflection (R500) and transmission (T500) modes yielded HQIs significantly lower than ATR (p < 0.0001 and p = 0.0010, respectively). In contrast, R100 was statistically equivalent to ATR (p = 0.9990), indicating comparable spectral quality. Figure 3 shows the distribution of spectral match quality (HQI%) obtained using ATR and µ-FTIR techniques applied to polymer particles of two different sizes (500 µm and 100 µm). A green highlight (HQI ≥ 80%) denotes reliable spectral identification, whereas the red zone (HQI < 70%) indicates insufficient spectral match quality. Results demonstrate that ATR yields consistently high HQI values, while µ-FTIR performance improves with smaller particle sizes (100 µm), especially in the reflectance mode. 3.2. Spectral patterns and material-specific responses The heatmap (Fig. 4 ) confirmed that ATR consistently produced high HQI values (> 90%) for PET, ABS, and EVA. In contrast, µFTIR performance for 500 µm particles was poor across several polymers. Reducing particle size to 100 µm improved results for PS, PET, and epoxy resin, highlighting the optical advantages of smaller, more uniform particles. ABS exhibited high HQIs across all modes and sizes, likely due to its homogeneous structure and low surface roughness. Conversely, PU, PC, and PMMA showed greater spectral variability, suggesting sensitivity to degradation, pigmentation, or multilayer effects (Rathore et al. 2023 ). A critical analysis of the data reveals that the ATR mode consistently yielded high performance for most polymers, particularly PET (97.2%), ABS (93.2%), and EVA (92.3%), all of which showed HQI values above 90%, indicating excellent spectral fidelity. In contrast, µFTIR modes analyzing larger particles (T500 and R500) showed the poorest performance, with HQI values below 70% for polymers such as PET, PS, epoxy resin, and PP — in some cases dropping below 60%. These findings highlight the limitations of analyzing larger particles using µFTIR, possibly due to optical interference associated with the thickness and surface topography of the material. Reducing the particle size to 100 µm (T100 and R100) resulted in substantial improvements in spectral quality, reversing the previously observed low performance. This effect is especially notable for PS, PET, and epoxy resin, which exceeded 90% HQI when analyzed as smaller particles in both µFTIR modes. These results confirmed that particle size is a critical factor in spectral accuracy, particularly for µFTIR techniques, where radiation penetration and surface scattering directly influence spectral resolution. Figure 5 show spectral performance comparison of µ-FTIR transmittance and reflection modes relative to ATR in 100 µm polymer particles The consistently high performance of ABS, with HQI values exceeding 90% across all modes and particle sizes, suggests favorable physicochemical properties and functional groups that result in high-quality spectra. Conversely, polymers such as PC, PU, and PMMA showed more inconsistent performance in µFTIR modes, reinforcing the need for a careful assessment of analytical conditions. Factors such as morphological heterogeneity, degradation history, pigmentation, and the presence of additives must be considered when selecting the appropriate spectroscopic technique, as they significantly affect spectral responses (Rathore et al. 2023 ). These findings align with Willans et al. ( 2023 ), who emphasize that the selection of a spectroscopic technique should take into account not only particle size, but also intrinsic material characteristics such as morphology, thickness, color, and associated matrix. Pigmented, rough, or multilayered particles tend to compromise the efficiency of transmission and reflection modes. In such cases, the DRIFTS (Diffuse Reflectance Infrared Fourier Transform Spectroscopy) technique has proven to be an effective alternative, as it better handles diffuse reflection and is more tolerant of morphological variability. Although µFTIR is more susceptible to spectral interferences—requiring strict control over sample preparation, substrate selection, and background acquisition—it offers significant advantages, including high spatial resolution and sensitivity to local compositional variations. These features enable exploratory scanning of the sample, allowing the identification of regions with morphological, chromatic, or structural differences. This is particularly valuable when analyzing heterogeneous, weathered, or biofilm-coated microplastics. As such, µFTIR is especially useful for detecting changes caused by environmental degradation, pigment heterogeneity, or biofilm presence—common features in environmental microplastics (Shi et al. 2024 ). While it is technically feasible to place larger particles (e.g., 500 µm) on the stage of FTIR microscopes, this practice is not recommended due to its detrimental effects on spectral quality. Larger particles often exhibit significant surface heterogeneity, including variations in thickness, roughness, opacity, and pigmentation, which can impair the uniform propagation of the infrared beam and, consequently, compromise the fidelity of the obtained spectra. In transmission mode, thick samples may excessively absorb infrared radiation, leading to detector saturation and the loss of important spectral bands. Additionally, irregular surfaces can cause light scattering, reducing the intensity of the transmitted signal and increasing spectral noise, thereby hindering accurate data interpretation. In reflection analyses, non-uniform surfaces of large particles can cause uncontrolled diffuse reflection, resulting in distorted and poorly reproducible spectra. Surface roughness and morphological variability make it difficult to achieve consistent specular reflections, which are essential for high-quality spectra. These advantages support the preferential use of particles ≤ 100 µm in µFTIR analyses to enhance spectral resolution and reproducibility, ultimately improving the overall quality of spectral data. In agreement with these findings, the ASTM D8333-20 standard and European Commission Decision 2017/848 formally recognize the applicability of µFTIR—particularly in reflection mode—for detecting microplastics smaller than 300 µm. These regulatory documents emphasize the importance of standardizing analytical parameters, such as minimum HQI thresholds and particle sizes compatible with the optical characteristics of the method, in order to ensure interlaboratory reproducibility and data comparability. However, despite acknowledging the role of particle morphology and size, these standards fall short of establishing explicit operational thresholds or spectral quality benchmarks—such as HQI values—leaving a methodological gap that challenges harmonization efforts across laboratories. In this context, the present findings support the need for evidence-based criteria, including the adoption of HQI ≥ 80% as a minimum benchmark for reliable identification of microplastics in reflection mode, particularly when analyzing particles around 100 µm. Based on the results obtained, it is proposed that HQI values ≥ 80%, obtained by µFTIR in reflectance mode using 100 µm particles, be adopted as a reliable threshold for spectral validation in microplastic identification protocols. This recommendation aims to standardize the required spectral quality and facilitate practical application in environmental monitoring programs. Additionally, it is recommended to use spectral libraries composed of environmentally representative polymers—accounting for real-world variations in color, surface morphology, and degradation—such as those available in the FLOPP and FLOPP-e libraries (De Frond et al. 2021), which have demonstrated significant improvements in correct identification rates for complex environmental samples. Overall, the lowest HQI values (< 65%) were recorded for 500 µm particles, while reducing the size to 100 µm led to consistent gains in spectral fidelity and lower variability among replicates—reinforcing the analytical superiority of fine particle use in high-resolution spectral protocols. These findings reinforce the importance of aligning the choice of spectroscopic technique with the morphological characteristics of the sample. Although most spectra did not exhibit classical degradation bands typically associated with oxidation (1700–1740 cm⁻¹) or hydrolysis (3200–3600 cm⁻¹), localized spectral alterations were observed in PMMA, PU, PET, and PP, suggesting the onset of degradation processes. These subtle changes are illustrated in the interferograms provided in the supplementary material (see interferogram in Supplementary Material). The comparative analysis revealed that ATR-FTIR consistently achieved superior performance in terms of spectral match quality. Mean HQI values exceeded 90% for polymers such as PET, ABS, and EVA, corroborating previous studies that support the robustness and reliability of the ATR method (Sorasan et al. 2022 ; Conterosito et al. 2025 ; Romeo & Diem 2005 ; Bassan et al. 2009 ). In contrast, µFTIR showed limited performance when applied to particles ≥ 500 µm, in both transmission (T500) and reflection (R500) modes. HQI values for PET, PS, epoxy resin, and PP often fell below 70%, likely due to optical artifacts resulting from excessive particle thickness, surface irregularities, or even early signs of degradation, as observed in the case of PET. These results highlight the need for optimized sample preparation and methodological adaptation when analyzing heterogeneous or weathered plastic fragments using µFTIR. Reducing the particle size to 100 µm (T100, R100) substantially improved µFTIR spectral quality, with HQI values exceeding 90% for PS, PET, and epoxy resin in both modes. This confirms particle size as a critical factor for spectral accuracy in µFTIR, since smaller particles enhance optical homogeneity and minimize scattering and diffuse reflection (Sorasan et al. 2022 ; Campanale et al. 2023 ). Although the reflectance mode in µFTIR is susceptible to optical artifacts and spectral distortions, it offers benefits such as reduced data acquisition time and higher throughput in environmental analyses. Visual overlay facilitated peak matching assessment, providing qualitative support for polymer identification, with particular attention to the fingerprint region (1850–700 cm⁻¹), recognized as the most specific and informative (Cowger et al. 2020 ). Even though reflectance spectra are harder to interpret than ATR-FTIR spectra, they still provide chemically relevant information, allowing identification of key absorption regions such as CH stretching (3000–2800 cm⁻¹), carbonyl (1820–1680 cm⁻¹), and NH stretching (3500–3300 cm⁻¹). These data support the exploratory potential of reflectance-FTIR, especially when supplemented by multivariate methods for interpretation (Xu et al. 2020 ). Among the twelve polymers analyzed, most exhibited well-preserved spectra comparable to virgin materials. Nylon showed stable bands—including N–H stretching (~ 3298 cm⁻¹), C = O (~ 1634 cm⁻¹), and multiple CH₂ bending vibrations—but presented minor variations attributed to moisture absorption (Noda et al. 2007 ; Jung et al. 2018 ). In epoxy resin, spectral comparison revealed clear evidence of oxidative and photochemical degradation, indicated by an increased C = O (~ 1730 cm⁻¹), a reduced aromatic C = C (~ 1600 cm⁻¹), and an increased O–H (~ 3400 cm⁻¹), suggesting formation of carbonyl groups, partial aromatic ring cleavage, and ether network scission (González et al. 2012 ). The FTIR spectrum of the polycarbonate (PC) sample displayed characteristic bands, including C = O (~ 1780 cm⁻¹), aromatic C = C (~ 1600 cm⁻¹), aromatic C–H (~ 2972 cm⁻¹), and CH₃ (~ 1405 cm⁻¹). However, a reduced intensity of the C = O band (~ 1780 cm⁻¹) was observed, indicating oxidative degradation of the material (Wang et al. 2022 ). Transmission mode produced high-quality spectra for thin particles, but thick (> 100 µm) or pigmented particles caused total IR beam absorption, compromising detection of absorption bands and leading to unidentifiable spectra. This reinforces the need to select acquisition mode based on sample and substrate properties, as unsuitable substrates may further degrade signal quality (Sorasan et al. 2022 ). Background spectra are essential to correct noise from water vapor, CO₂, or substrates, preventing misinterpretation from environmental or material interferences. µFTIR coupled with optical microscopy inherently has a spatial resolution limit (~ 10 µm), defined by the diffraction limit and Rayleigh’s criterion, influenced by the objective lens’s numerical aperture. The choice between reflectance or transmission mode in µFTIR depends on the substrate used for particle deposition, directly affecting spectral quality and feasibility (Sorasan et al. 2022 ). These results highlight methodological limitations in extracting chemical information solely from spectral data, which can hinder accurate polymer classification—particularly in environmentally weathered samples. However, such limitations can be mitigated through the integration of spectroscopic data with multivariate analytical approaches (e.g., PCA, Random Forest), which enhance classification accuracy and robustness (Silva et al. 2020 ; Liu et al. 2024 ; Villegas-Camacho et al. 2025 ; Conterosito et al. 2025 ). 3.3. Analytical grouping of polymers by HQI behavior Based on HQI values across techniques and sizes, polymers were grouped into three categories: Group 1 : ABS, PMMA, and PVC – high consistency and low CV (< 10%), reflecting stable performance. Group 2 : PE, PP, PS, PET, EVA, and epoxy resin – strong improvement in 100 µm analysis; highly affected by size and acquisition mode. Group 3 : Nylon, PU, and PC – best performance under ATR; µFTIR results were variable, particularly at 500 µm. Polymers were grouped based on spectral performance. The first group—ABS, PMMA, and PVC—showed high consistency, with mean HQI values above 80% and coefficients of variation (CV) below 10% across techniques and particle sizes. ABS stood out, achieving a mean HQI of 86.6% and a CV of 7.6%, reflecting high reproducibility, likely due to favorable optical properties such as low surface roughness, controlled opacity, and homogeneous chemical composition (Rathore et al. 2023 ; Cowger et al. 2020 ). The second group—PE, PP, PS, PET, EVA, and epoxy resin—performed better at 100 µm, particularly under µFTIR. PS, for example, reached an HQI of 62.5% at R500 but improved to 94.5% at R100. Epoxy resin showed the lowest individual HQI (56.2% at R500) but achieved satisfactory values in T100 (93.7%) and R100 (91.1%). These polymers were more sensitive to analytical conditions, with higher CVs (e.g., PET: 21.9%; PS: 16.5%; epoxy resin: 16.9%). PET exhibited substantial variation across spectroscopic techniques, highlighting the influence of acquisition mode and particle size on identification. Although it achieved 97.2% HQI in ATR, PET had unsatisfactory values in T500 (59.4%) and R500 (59.5%), improving at T100 (82.8%). Spectra showed broadening of the O–H band (~ 3300 cm⁻¹), carbonyl changes (~ 1720 cm⁻¹), and loss of definition in 1500–900 cm⁻¹, suggesting mild degradation from oxidation or hydrolysis (Sammon et al. 2000; Du et al. 2014). The third group—nylon, PU, and PC—performed better under ATR, with lower HQIs and greater variability in µFTIR, especially reflection at 500 µm. PU showed an HQI of 80% in ATR but dropped to 69.2% in R500, possibly reflecting chemical degradation. Spectral changes included intensified carbonyl (~ 1720 cm⁻¹) and attenuated N–H (~ 1530 cm⁻¹), indicating oxidation and urethane chain hydrolysis (Jung et al. 2018 ; Noda et al. 2007 ). Polycarbonate (PC) achieved HQI values from 70.5–83.1%, ensuring satisfactory identification without exceptional performance. Factors such as multilayer structure, pigments, and surface impurities likely reduced spectral quality (Willans et al. 2023 ). Preliminary analysis yielded HQI < 60% for PC; however, reanalysis with another particle surpassed the acceptance threshold, illustrating intra-sample heterogeneity’s impact on result variability (Käppler et al. 2016 ; Silva et al. 2020 ). These results show that while spectra retained sufficient markers for identification, oxidative degradation, hydrolysis, or photo-oxidation introduced structural changes, potentially reducing spectral similarity with virgin polymer standards. These chemical alterations (e.g., formation of carbonyl and hydroxyl groups) increase chemical and physical heterogeneity, leading to new bands, broadening, reduced peak definition, and decreased relative intensities—compromising HQI values, especially in µFTIR. Additionally, these modifications amplify scattering and diffuse reflection in reflectance mode or cause total absorption in transmission mode, resulting in masked or distorted spectra. These findings emphasize the need for spectral libraries representing environmentally degraded polymers and complementary multivariate approaches for accurate identification. Overall, most polymers retained spectra comparable to virgin materials. However, alterations from oxidative degradation, moisture absorption, and photochemical processes hindered precise identification. These variations are especially relevant for environmental samples exposed to weathering, biofilm formation, and contaminant adsorption (Lee et al. 2023 ; Ivleva 2021 ). Integrating multiple analytical modes helps overcome limitations from heterogeneous surfaces and morphological irregularities (Tanoiri et al. 2023 ; Rathore et al. 2023 ). In this context, coupling µFTIR with machine learning (ML) algorithms offers promising improvements in spectral classification, especially for degraded environmental samples. PCA, Random Forest, and SVM approaches have enhanced automated classification and mitigated baseline distortions and pigment interference (Silva et al. 2020 ; Liu et al. 2024 ; Villegas-Camacho et al. 2025 ). Confirmatory techniques such as Raman microspectroscopy, DRIFTS, or Py-GC-MS can further increase analytical robustness under real-world conditions (Cowger et al. 2020 ; Primpke et al. 2017 ; Primpke et al. 2020 ). This study presents experimental evidence of the limitations of µFTIR for analyzing microplastic particles ≥ 500 µm. The results show a significant decrease in HQI values for PET, PS, PP, and epoxy resin compared to ATR-FTIR, reinforcing the latter's superior reproducibility and spectral fidelity for thick or morphologically complex particles. These findings help address gaps in ISO 24187:2023 and ASTM D8333-20, which acknowledge the influence of morphology but do not establish quantitative spectral quality criteria or clear particle size thresholds. Our data support adopting HQI ≥ 80% as a minimum validation criterion for µFTIR reflectance analysis of 100 µm particles, contributing to the standardization of analytical protocols for environmental microplastic monitoring. Although the standards recommend adapting the acquisition mode (transmission, reflection, or ATR) to the physical and chemical properties of the sampl - such as thickness, roughness, and pigmentation- they do not specify ideal particle size ranges or corrective measures for thick particles. Section 6.4.2 of ISO 24187:2023, for instance, advises choosing the acquisition mode based on morphology but lacks objective operational parameters. This study helps fill that gap by empirically demonstrating the reduced reliability of µFTIR for particles ≥ 500 µm, as evidenced by HQI values frequently falling below the acceptance threshold (≥ 70%). While this guidance recognizes the influence of thickness and morphology on analysis, the standard does not define an ideal particle size range for µFTIR use, nor does it suggest corrective measures for thick particles (> 300–500 µm). Therefore, this study addresses this practical gap by empirically demonstrating that µFTIR loses reliability for particles ≥ 500 µm, as evidenced by HQI values falling below the automatic acceptance threshold (≥ 70%). Similarly, ASTM D8333-20, which focuses primarily on sample preparation (chemical and enzymatic digestion), does not discuss the impacts of thickness or roughness on spectral quality, nor does it provide recommendations for selecting the optimal acquisition mode based on particle size. The standard assumes that, once organic interferents have been removed, the sample is ready for FTIR or Raman analysis, without addressing the inherent optical limitations of µFTIR when applied to large, heterogeneous particles. These results underscore the need for more explicit quantitative guidelines in international standards, particularly regarding the application of µFTIR to environmentally relevant, morphologically heterogeneous microplastics. By offering experimental evidence and HQI-based acceptance parameters, this study contributes to ongoing efforts toward methodological harmonization and to improving analytical practices for environmental microplastic monitoring. 3.4. Practical implications and methodological guidance Our results confirm that µFTIR is susceptible to spectral distortions in large or morphologically complex particles, particularly in transmission mode. Small particles (≤ 100 µm) offer superior resolution and lower variability, supporting their use in standard protocols. Despite µFTIR’s sensitivity to optical artifacts, its high spatial resolution and ability to detect local compositional changes remain valuable—particularly for weathered, pigmented, or biofilm-coated microplastics (Shi et al. 2024 ). Combining µFTIR with multivariate tools (e.g., PCA, Random Forest) and complementary methods (e.g., DRIFTS, Raman, Py-GC-MS) can enhance accuracy under environmental conditions (Liu et al. 2024 ; Conterosito et al. 2025 ). By integrating spectral, morphological, and statistical analyses, this study offers quantitative evidence to support HQI-based decision criteria and promotes methodological harmonization in microplastic identification. Conclusions This study demonstrated that both FTIR acquisition mode and particle size are critical factors in the spectral identification of fragmented microplastics. ATR-FTIR consistently delivered high HQI values for particles ≥ 500 µm, particularly for PET, ABS, and EVA, confirming its robustness for larger, thicker fragments. In contrast, µFTIR showed reduced spectral performance with larger particles, while analysis of 100 µm fragments, especially in reflection mode (R100), achieved HQI values comparable to ATR-FTIR. Additionally, the use of ZnSe windows proved effective for µFTIR analysis, ensuring high spectral transmission and reproducibility even with smaller particles—an aspect still scarcely reported in the literature and which reinforces their potential for broader adoption in standardized protocols. These findings underscore the need to align analytical strategies with the morphological and physical properties of microplastics to ensure accurate polymer identification. Furthermore, they highlight the importance of methodological standardization—including optimal particle size selection, acquisition mode, and optical components—to improve reproducibility and comparability across studies. This work offers practical insights to refine analytical protocols and supports ongoing efforts to harmonize microplastic monitoring methodologies in environmental research, ultimately enabling more accurate and reliable assessments of microplastic pollution across diverse environmental matrices. Declarations Acknowledgements The authors would like to thank the Rio de Janeiro State Research Support Foundation (FAPERJ) for financial support through research grants under process numbers E-26/203.421/2023, E-26/210.769/2021, and E-26/201.111/2021. Funding Statement The authors would like to thank the Rio de Janeiro State Research Support Foundation (FAPERJ) for financial support through research grants under process numbers E-26/203.421/2023, E-26/210.769/2021, and E-26/201.111/2021. Author Contributions Barbara Rodrigues Geraldino: Conceptualization, Formal analysis, Investigation, Methodology, Validation, Writing – original draft, Writing – review & editing Stella Melgaço: Methodology, Writing – original draft Daniele Bila Maia: Supervision, Validation, Writing – review & editing Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Ethical Approval* This is not applicable Consent to Participate* This is not applicable Consent to Publish* This is not applicable Competing Interests The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Data Availability Statement All data supporting the findings of this study are included in this article and its supplementary information files. 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Environ Sci Adv 2(4):663–674. https://doi.org/10.1039/D2VA00313A Xu J-L, Hassellöv M, Yu K, Gowen AA (2020) Microplastic Characterization by Infrared Spectroscopy. In: Rocha-Santos T, Costa M, Mouneyrac C (eds) Handbook of Microplastics in the Environment. Springer International Publishing, Cham, pp 1–33 Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6683637","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":460310855,"identity":"4664a825-0cec-4972-89f9-318aed9573b5","order_by":0,"name":"Barbara Geraldino","email":"data:image/png;base64,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","orcid":"https://orcid.org/0000-0002-7061-1574","institution":"Rio de Janeiro State University: Universidade do Estado do Rio de Janeiro","correspondingAuthor":true,"prefix":"","firstName":"Barbara","middleName":"","lastName":"Geraldino","suffix":""},{"id":460310856,"identity":"8d54978c-0796-4372-9b69-b1849cbf9741","order_by":1,"name":"Stella Melgaço","email":"","orcid":"","institution":"Rio de Janeiro State University: Universidade do Estado do Rio de Janeiro","correspondingAuthor":false,"prefix":"","firstName":"Stella","middleName":"","lastName":"Melgaço","suffix":""},{"id":460310857,"identity":"c69a6fa2-56e2-45df-ac03-ba99d40778c7","order_by":2,"name":"Daniele Maia Bila","email":"","orcid":"","institution":"Rio de Janeiro State University: Universidade do Estado do Rio de Janeiro","correspondingAuthor":false,"prefix":"","firstName":"Daniele","middleName":"Maia","lastName":"Bila","suffix":""}],"badges":[],"createdAt":"2025-05-16 22:35:08","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6683637/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6683637/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":84185985,"identity":"c2065ce7-2a16-4b4d-9489-769fdb63f752","added_by":"auto","created_at":"2025-06-09 05:33:41","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":418906,"visible":true,"origin":"","legend":"\u003cp\u003eSchematic representation of the analytical methods applied for microplastic identification in environmental matrices. ATR-FTIR and µFTIR (in transmittance and reflectance modes) were compared for the spectral characterization of 12 commercial polymers at different particle sizes (100 µm and ≥500 µm), using the Hit Quality Index (HQI ≥ 70%) as a performance metric.\u003c/p\u003e","description":"","filename":"Figure11.png","url":"https://assets-eu.researchsquare.com/files/rs-6683637/v1/8485fef14313f04f5ab4d436.png"},{"id":84185957,"identity":"b06fe430-4456-4f51-9de1-f408f64d7b74","added_by":"auto","created_at":"2025-06-09 05:33:33","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":21115,"visible":true,"origin":"","legend":"\u003cp\u003eHit Quality Index (HQI) obtained via ATR-FTIR for each polymer analyzed.\u003c/p\u003e","description":"","filename":"Figure21.png","url":"https://assets-eu.researchsquare.com/files/rs-6683637/v1/ac53a82c6ec1c6b772cdde96.png"},{"id":84185955,"identity":"b62e22b7-12ba-4578-8cf8-099f094525d5","added_by":"auto","created_at":"2025-06-09 05:33:27","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":81627,"visible":true,"origin":"","legend":"\u003cp\u003eComparative spectral performance (HQI%) of ATR and μ-FTIR techniques across polymer particles of 500 µm and 100 µm.\u003c/p\u003e","description":"","filename":"Figure31.png","url":"https://assets-eu.researchsquare.com/files/rs-6683637/v1/716b0662c6e0dfec91b301af.png"},{"id":84185952,"identity":"8d3bf316-6d3e-4d57-962b-f44951e989e7","added_by":"auto","created_at":"2025-06-09 05:32:50","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":206280,"visible":true,"origin":"","legend":"\u003cp\u003eHeatmap of HQI values for different polymers analyzed using ATR and µFTIR (transmission and reflection modes) at 500 µm and 100 µm particle sizes.\u003c/p\u003e","description":"","filename":"Figure41.png","url":"https://assets-eu.researchsquare.com/files/rs-6683637/v1/af30614f445961e9184cc770.png"},{"id":84185948,"identity":"e482f782-c17e-472b-886c-8d54efbfba80","added_by":"auto","created_at":"2025-06-09 05:32:45","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":172279,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of difference compared to ATR (100 µm particles)\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-6683637/v1/498206de50cc3e24f84471fa.png"},{"id":86311588,"identity":"706b5928-b92a-47eb-bcca-e7280f65e9c3","added_by":"auto","created_at":"2025-07-09 08:12:16","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1475289,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6683637/v1/09c5baaa-d7d2-4bfc-85a9-96e492d11455.pdf"}],"financialInterests":"","formattedTitle":"Harmonizing infrared spectroscopic techniques for microplastic identification: a comparative evaluation of ATR and µFTIR transmission and reflection modes","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe presence of microplastics (MPs) has been increasingly reported across a wide range of environmental matrices\u0026mdash;from deep-sea sediments to remote locations such as Antarctic snow (Jones-Williams et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Global surveys have identified MPs in marine environments (Mutuku et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Pegado et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), surface waters (Hoehn et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Trindade et al. \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), air (Ortega and Cort\u0026eacute;s-Arriagada \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Ferraz et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), soil (Yang et al. 2022; Sajjad et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), freshwater lakes (Dong et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Chen et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), food items (Kumar et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Alberghini et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), bottled water (Al-Mansoori et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Weisser et al. 2021; Li et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), salt (Di Fiore et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), aquatic organisms (Nodehi et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Liu et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), and even in human tissues\u0026mdash;including the brain, atheromas, and placenta (Nihart et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Amato-Louren\u0026ccedil;o et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Ragusa et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Sharma et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Marfella et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Reported concentrations range from as low as 2.5 particles per cubic meter in ocean surface waters (C\u0026oacute;zar et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) to over 8,500 particles per liter in urban runoff and sewer overflow events (Sun et al. \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), highlighting the pervasive and heterogeneous nature of microplastic pollution.\u003c/p\u003e \u003cp\u003eAccurate detection and quantification of MPs and nanoplastics remain analytically challenging due to the complexity of environmental matrices and the physicochemical diversity of the particles (Rani et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Jim\u0026eacute;nez-Lamana et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Huang et al. 2023). Their small size, morphological heterogeneity, and surface contamination complicate differentiation from natural materials and reduce identification accuracy (Jia et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Fu et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Biofilm formation and adsorption of organic matter may obscure spectral features, while pigments and additives can interfere with polymer-specific signals. Sampling methods can also introduce biases: Lindeque et al. (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) showed that 100 \u0026micro;m mesh nets captured 2.5 times more MPs than 333 \u0026micro;m nets and 10 times more than 500 \u0026micro;m nets, demonstrating that smaller particles\u0026mdash;arguably the most relevant\u0026mdash;are often underrepresented in environmental monitoring protocols (Sharma et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOptical microscopy remains commonly used for initial screening based on shape, color, and size (He et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Kumar et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Barcel\u0026oacute; et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). However, it is time-consuming, subjective, and highly error-prone, particularly for small or transparent particles. Misidentification rates exceeding 70% have been reported, especially involving cellulose fibers, organic debris, and mineral fragments (Shim et al. \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFourier-transform infrared (FTIR) spectroscopy is one of the most widely used techniques for MP identification, offering non-destructive chemical characterization with high specificity. Analyses can be conducted in Attenuated Total Reflectance (ATR) mode\u0026mdash;which requires direct contact with the ATR crystal\u0026mdash;or via micro-FTIR (\u0026micro;FTIR), which integrates infrared detection with optical microscopy. \u0026micro;FTIR can operate in either transmission or reflection mode, depending on how the IR beam interacts with the sample and its substrate (Maurizi et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Each acquisition mode exhibits different sensitivities to variables such as thickness, morphology, pigmentation, and degradation. Polymer identification relies on spectral matching against reference libraries (Primpke et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), but spectral quality may vary depending on acquisition mode and sample features.\u003c/p\u003e \u003cp\u003eMany studies use a single acquisition mode without evaluating how polymer morphology, surface characteristics, or weathering impact spectral fidelity. This limits cross-study reproducibility and hinders progress toward harmonized monitoring protocols. Moreover, current standards\u0026mdash;such as ASTM D8333-20 and ISO 24187:2023\u0026mdash;acknowledge the role of sample morphology but lack quantitative guidance on particle size limits or spectral quality benchmarks, such as the Hit Quality Index (HQI).\u003c/p\u003e \u003cp\u003eTo address these limitations, the present study offers a systematic evaluation of the analytical performance of ATR and \u0026micro;FTIR\u0026mdash;operated in both transmission and reflection modes\u0026mdash;on twelve polymers commonly found in consumer products. Fragmented particles of standardized sizes were analyzed under consistent experimental conditions, including identical spectrometer settings and validation criteria based on HQI and expert visual inspection. By investigating how particle size and spectroscopic mode affect spectral fidelity and polymer classification, this study contributes to ongoing efforts toward methodological harmonization and supports the development of robust protocols for identifying environmentally relevant microplastics. These insights are particularly valuable for improving the reliability of environmental monitoring programs and interlaboratory comparability.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Sample preparation\u003c/h2\u003e \u003cp\u003eTwelve polymers were selected to represent a wide range of plastic materials commonly encountered in consumer products and environmental samples. These included polyethylene (PE), polystyrene (PS), polypropylene (PP), acrylonitrile butadiene styrene (ABS), polyethylene terephthalate (PET), ethylene-vinyl acetate (EVA), nylon, polycarbonate (PC), polyurethane (PU), polyvinyl chloride (PVC), epoxy resin, and polymethyl methacrylate (PMMA). The diversity in chemical composition among these materials reflects the typical heterogeneity found in environmental microplastic pollution.\u003c/p\u003e \u003cp\u003eAll polymers were sourced from commercially available household items, with product type, manufacturer, and brand recorded for traceability. Each product was inspected to ensure the absence of surface coatings, composite structures, or multilayered elements prior to sample processing. Plastic fragments were manually cut using stainless-steel scissors cleaned with ethanol and deionized water to prevent cross-contamination. Edges were standardized by sanding with fine-grit abrasive paper, both to simulate mechanical weathering and to reduce heterogeneity in thickness and surface roughness. Fragments were rinsed three times with ultrapure deionized water and stored in sterile glass Petri dishes until analysis.\u003c/p\u003e \u003cp\u003ePreliminary visual inspection was performed under a stereomicroscope (10\u0026times; to 20\u0026times; magnification) in a clean workspace to ensure the absence of visible contaminants or extraneous particles. All tools and containers were pre-cleaned and rinsed three times with ultrapure water. Procedural blanks were maintained throughout sample preparation to monitor potential contamination.\u003c/p\u003e \u003cp\u003eFragments approximately 500 \u0026micro;m and 100 \u0026micro;m in size were selected using a calibrated scale affixed to the microscope stage. Individual particles were transferred using ethanol-cleaned, antistatic titanium tweezers and stored in sealed, pre-cleaned glass vials to avoid particle loss or contamination before spectroscopic analysis. An overview of the methodological approach used for microplastic identification is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, which illustrates the main analytical procedures, the types of polymers assessed, the particle size ranges, and the spectral acquisition modes compared (ATR-FTIR and \u0026micro;FTIR in transmission and reflection).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Analytical Techniques\u003c/h2\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003e2.1.1.ATR\u003c/h2\u003e \u003cp\u003eInitial polymer characterization was conducted using Attenuated Total Reflectance Fourier-Transform Infrared (ATR-FTIR) spectroscopy (IRTracer-100, Shimadzu). Twelve 500 \u0026micro;m particles (one per polymer) were analyzed using a diamond ATR crystal. Each spectrum was acquired using 45 scans, over the 400\u0026ndash;4000 cm⁻\u0026sup1; range, at 4 cm⁻\u0026sup1; resolution. Instrument control and data processing were performed using LabSolutions IR software. Baseline correction was applied using the built-in algorithm, which fits a curve through spectral minima to reduce background deviations. The Happ-Genzel apodization function was used, as recommended for spectroscopic analysis of heterogeneous materials (Andrade et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Willans et al. \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.1.2. \u0026micro;FTIR- Transmission and Reflection Modes\u003c/h2\u003e \u003cp\u003eTo compare spectral performance across acquisition modes, the same set of polymers was analyzed via micro-FTIR (\u0026micro;FTIR) using both transmission and reflection modes. Particles of ~\u0026thinsp;500 \u0026micro;m and 100 \u0026micro;m were placed on zinc selenide (ZnSe) windows, selected for their high infrared transparency and minimal spectral interference.\u003c/p\u003e \u003cp\u003eMeasurements were performed using a Shimadzu AIM-9000 system, equipped with a liquid nitrogen-cooled mercury cadmium telluride (MCT) detector. Instrument parameters matched those used in ATR-FTIR analysis. Background spectra were collected separately for each acquisition mode to minimize environmental interferences (Andrade et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Willans et al. \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The aperture size was set to 25 \u0026times; 25 \u0026micro;m.\u003c/p\u003e \u003cp\u003eSpectral post-processing involved baseline correction and subtraction of atmospheric CO₂ bands, using AIMsolution software. All settings were standardized to maintain comparability across techniques and support harmonized reporting practices in microplastic FTIR analysis (Munno et al. 2020; Kozloski et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Evaluation of Results Consistency\u003c/h2\u003e \u003cp\u003eTo ensure analytical reliability, each sample was measured in triplicate under identical conditions. Spectra were accepted only if the variation in Hit Quality Index (HQI) across replicates remained below 10%, serving as a quality control threshold aligned with best practices in polymer spectral analysis (Andrade et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSpectral identification was performed by matching measured spectra against three reference libraries (ATR-Polymer2, IRs-Polymer2, and T-Polymer2). A minimum HQI of 80% (800/1000) was adopted as the acceptance threshold, based on prior studies (Primpke et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Clough et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). While ASTM D8333-20 does not mandate a fixed HQI value, the \u0026ge;\u0026thinsp;80% cutoff has been validated as a reliable indicator of spectral match quality in recent literature. Additional performance verification was conducted using standardized polyethylene microspheres (63\u0026ndash;75 \u0026micro;m, Cospheric, Santa Barbara, CA, USA), which served as internal controls to assess system stability and spectral reproducibility (ASTM, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eStatistical analysis was performed using SPSS version 20. HQI values were compared across acquisition modes using one-way ANOVA, followed by Tukey\u0026rsquo;s post hoc test for multiple comparisons. The coefficient of variation (CV) was also calculated to evaluate intra-group variability. A significance threshold of p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was adopted for all statistical tests.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results and Discussion","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Comparison between spectroscopic modes\u003c/h2\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e3.1.1. Performance of the ATR Technique\u003c/h2\u003e \u003cp\u003eATR-FTIR, applied in this study to analyze plastic particles ≥ 500 µm, demonstrated robust and consistent performance in identifying all twelve evaluated polymers. Each material achieved HQI values above 80% (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), indicating excellent agreement with reference spectral libraries (Cowger et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Gouda et al. 2025).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAlthough ASTM D8333-20 is widely used as a reference for microplastic spectral analysis via µFTIR, it does not specify a fixed HQI threshold as a universal acceptance criterion. Instead, it emphasizes manual validation by experienced analysts, particularly for ambiguous spectra or potential interferences. In practice, however, HQI values ≥ 80% are commonly considered reliable for automatic acceptance, provided the polymer’s characteristic bands are confirmed. Scores between 70% and 79% generally require manual inspection of key bands, while values below 70% are typically rejected or require complementary techniques such as Raman spectroscopy or microscopy (Whiting et al. 2022).\u003c/p\u003e \u003cp\u003eNotably, PET (97.2%), ABS (93.2%), EVA (92.3%), PP (90.5%), and PVC (90.2%) achieved HQI values exceeding 90%, with replicate variation \u0026lt; 10%, indicating high reproducibility and analytical reliability.\u003c/p\u003e \u003cp\u003eThese findings align with De Frond et al. (2021), who reported HQI values \u0026gt; 80% for fragments derived from everyday plastic products. Similarly, Whiting et al. (2022) validated HQI ≥ 70% as an acceptance criterion for automated FTIR identification, achieving up to 88.3% accuracy—reinforcing ATR’s suitability for reliable laboratory screening of microplastics.\u003c/p\u003e \u003cp\u003ePolymers with slightly lower performance, such as PU (80%), PE (82.5%), PC (83.1%), and nylon (87.8%), still remained within the acceptable range. Meanwhile, PS (84.8%), PMMA (85.2%), and epoxy resin (85.3%) also showed satisfactory HQIs, although their spectral analyses may be more sensitive to morphological and physicochemical factors—including thickness, pigmentation, degradation, and surface roughness—previously reported to affect spectral quality (Käppler et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Cowger et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Invernizzi et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSpectral quality in ATR is directly dependent on effective coupling between the sample and the ATR crystal. According to Cowger et al. (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), thick or opaque samples improve optical coupling and tend to yield better-defined spectra. Conversely, rough, deformed, or irregular surfaces hinder uniform contact with the crystal, increasing infrared scattering and reducing spectral resolution (Invernizzi et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Diaz \u0026amp; Thériault \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eInadequate spectral preprocessing can also amplify artifacts that interfere with automated identification. As Willans et al. (\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) noted, samples with irregular surface topography often produce distorted bands, peak shifts, and baseline variations due to diffuse reflection, compromising spectral fidelity. Therefore, strict quality control and spectral correction procedures are recommended, particularly for environmental analyses.\u003c/p\u003e \u003cp\u003eImportantly, results with synthetic particles were consistent with preliminary findings from heterogeneous environmental samples, supporting ATR-FTIR’s potential—when thickness, cleanliness, and contact are properly controlled—for identifying microplastics in real-world scenarios.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e3.1.2. Performance of the µFTIR Technique\u003c/h2\u003e \u003cp\u003eThe application of µFTIR in this study revealed significant differences in performance between reflection and transmission modes, particularly as a function of particle size. For 100 µm fragments, reflection mode yielded slightly higher HQI values—often exceeding 85%—indicating high spectral fidelity. In contrast, both modes showed a marked decline in performance when analyzing 500 µm particles, with HQIs dropping below 70% for several polymers.\u003c/p\u003e \u003cp\u003eRecent studies support these findings. Sefiloglu et al. (2024) and Silva et al. (\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) highlighted µFTIR’s high sensitivity for small particles, while Ivleva (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) emphasized the need for integrated analytical approaches to address morphological complexity and degradation in environmental microplastics. Rathore et al. (\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) also demonstrated the effectiveness of reflection mode for particles ≤ 100 µm, reporting HQI values above 70% for polymers like PE and PET. In this study, ZnSe slides were used as optical substrates to enhance mid-infrared transmission (Liu et al. 2023; Emir et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), yet performance remained lower than reflection mode for small particles.\u003c/p\u003e \u003cp\u003eDespite reflection mode’s relatively strong performance for small particles, certain limitations must be considered. The overlap of specular and diffuse reflection components can distort spectra, affecting band shape, intensity, and position (Cowger et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Invernizzi et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Spectral performance is closely tied to the sample’s physical and optical properties: thick or opaque particles are better suited to ATR, while thin and translucent fragments typically perform better in µFTIR, especially in transmission mode. Irregular topography—such as rough or deformed surfaces—can also reduce spectral resolution, as shown by Diaz and Thériault (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). For complex structures like multilayered materials (e.g., CD-grade polycarbonate), ASTM D8333-20 specifically recommends µFTIR reflection mode due to its ability to accommodate morphological overlap and variation (Huber et al. 2020; ASTM \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e\u003cem\u003e3.1.3.\u003c/em\u003e Comparative performance and reproducibility\u003c/h2\u003e \u003cp\u003eStatistical analysis confirmed that µFTIR performance is significantly affected by particle size. For 500 µm particles, both reflection (R500) and transmission (T500) modes yielded HQIs significantly lower than ATR (p \u0026lt; 0.0001 and p = 0.0010, respectively). In contrast, R100 was statistically equivalent to ATR (p = 0.9990), indicating comparable spectral quality. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows the distribution of spectral match quality (HQI%) obtained using ATR and µ-FTIR techniques applied to polymer particles of two different sizes (500 µm and 100 µm). A green highlight (HQI ≥ 80%) denotes reliable spectral identification, whereas the red zone (HQI \u0026lt; 70%) indicates insufficient spectral match quality. Results demonstrate that ATR yields consistently high HQI values, while µ-FTIR performance improves with smaller particle sizes (100 µm), especially in the reflectance mode.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Spectral patterns and material-specific responses\u003c/h2\u003e \u003cp\u003eThe heatmap (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) confirmed that ATR consistently produced high HQI values (\u0026gt; 90%) for PET, ABS, and EVA. In contrast, µFTIR performance for 500 µm particles was poor across several polymers. Reducing particle size to 100 µm improved results for PS, PET, and epoxy resin, highlighting the optical advantages of smaller, more uniform particles.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eABS exhibited high HQIs across all modes and sizes, likely due to its homogeneous structure and low surface roughness. Conversely, PU, PC, and PMMA showed greater spectral variability, suggesting sensitivity to degradation, pigmentation, or multilayer effects (Rathore et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eA critical analysis of the data reveals that the ATR mode consistently yielded high performance for most polymers, particularly PET (97.2%), ABS (93.2%), and EVA (92.3%), all of which showed HQI values above 90%, indicating excellent spectral fidelity. In contrast, µFTIR modes analyzing larger particles (T500 and R500) showed the poorest performance, with HQI values below 70% for polymers such as PET, PS, epoxy resin, and PP — in some cases dropping below 60%. These findings highlight the limitations of analyzing larger particles using µFTIR, possibly due to optical interference associated with the thickness and surface topography of the material.\u003c/p\u003e \u003cp\u003eReducing the particle size to 100 µm (T100 and R100) resulted in substantial improvements in spectral quality, reversing the previously observed low performance. This effect is especially notable for PS, PET, and epoxy resin, which exceeded 90% HQI when analyzed as smaller particles in both µFTIR modes. These results confirmed that particle size is a critical factor in spectral accuracy, particularly for µFTIR techniques, where radiation penetration and surface scattering directly influence spectral resolution. Figure\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e show spectral performance comparison of µ-FTIR transmittance and reflection modes relative to ATR in 100 µm polymer particles\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe consistently high performance of ABS, with HQI values exceeding 90% across all modes and particle sizes, suggests favorable physicochemical properties and functional groups that result in high-quality spectra. Conversely, polymers such as PC, PU, and PMMA showed more inconsistent performance in µFTIR modes, reinforcing the need for a careful assessment of analytical conditions. Factors such as morphological heterogeneity, degradation history, pigmentation, and the presence of additives must be considered when selecting the appropriate spectroscopic technique, as they significantly affect spectral responses (Rathore et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThese findings align with Willans et al. (\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), who emphasize that the selection of a spectroscopic technique should take into account not only particle size, but also intrinsic material characteristics such as morphology, thickness, color, and associated matrix. Pigmented, rough, or multilayered particles tend to compromise the efficiency of transmission and reflection modes. In such cases, the DRIFTS (Diffuse Reflectance Infrared Fourier Transform Spectroscopy) technique has proven to be an effective alternative, as it better handles diffuse reflection and is more tolerant of morphological variability.\u003c/p\u003e \u003cp\u003eAlthough µFTIR is more susceptible to spectral interferences—requiring strict control over sample preparation, substrate selection, and background acquisition—it offers significant advantages, including high spatial resolution and sensitivity to local compositional variations. These features enable exploratory scanning of the sample, allowing the identification of regions with morphological, chromatic, or structural differences. This is particularly valuable when analyzing heterogeneous, weathered, or biofilm-coated microplastics. As such, µFTIR is especially useful for detecting changes caused by environmental degradation, pigment heterogeneity, or biofilm presence—common features in environmental microplastics (Shi et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWhile it is technically feasible to place larger particles (e.g., 500 µm) on the stage of FTIR microscopes, this practice is not recommended due to its detrimental effects on spectral quality. Larger particles often exhibit significant surface heterogeneity, including variations in thickness, roughness, opacity, and pigmentation, which can impair the uniform propagation of the infrared beam and, consequently, compromise the fidelity of the obtained spectra.\u003c/p\u003e \u003cp\u003eIn transmission mode, thick samples may excessively absorb infrared radiation, leading to detector saturation and the loss of important spectral bands. Additionally, irregular surfaces can cause light scattering, reducing the intensity of the transmitted signal and increasing spectral noise, thereby hindering accurate data interpretation. In reflection analyses, non-uniform surfaces of large particles can cause uncontrolled diffuse reflection, resulting in distorted and poorly reproducible spectra. Surface roughness and morphological variability make it difficult to achieve consistent specular reflections, which are essential for high-quality spectra.\u003c/p\u003e \u003cp\u003eThese advantages support the preferential use of particles ≤ 100 µm in µFTIR analyses to enhance spectral resolution and reproducibility, ultimately improving the overall quality of spectral data. In agreement with these findings, the ASTM D8333-20 standard and European Commission Decision 2017/848 formally recognize the applicability of µFTIR—particularly in reflection mode—for detecting microplastics smaller than 300 µm. These regulatory documents emphasize the importance of standardizing analytical parameters, such as minimum HQI thresholds and particle sizes compatible with the optical characteristics of the method, in order to ensure interlaboratory reproducibility and data comparability. However, despite acknowledging the role of particle morphology and size, these standards fall short of establishing explicit operational thresholds or spectral quality benchmarks—such as HQI values—leaving a methodological gap that challenges harmonization efforts across laboratories. In this context, the present findings support the need for evidence-based criteria, including the adoption of HQI ≥ 80% as a minimum benchmark for reliable identification of microplastics in reflection mode, particularly when analyzing particles around 100 µm.\u003c/p\u003e \u003cp\u003eBased on the results obtained, it is proposed that HQI values ≥ 80%, obtained by µFTIR in reflectance mode using 100 µm particles, be adopted as a reliable threshold for spectral validation in microplastic identification protocols.\u003c/p\u003e \u003cp\u003eThis recommendation aims to standardize the required spectral quality and facilitate practical application in environmental monitoring programs. Additionally, it is recommended to use spectral libraries composed of environmentally representative polymers—accounting for real-world variations in color, surface morphology, and degradation—such as those available in the FLOPP and FLOPP-e libraries (De Frond et al. 2021), which have demonstrated significant improvements in correct identification rates for complex environmental samples.\u003c/p\u003e \u003cp\u003eOverall, the lowest HQI values (\u0026lt; 65%) were recorded for 500 µm particles, while reducing the size to 100 µm led to consistent gains in spectral fidelity and lower variability among replicates—reinforcing the analytical superiority of fine particle use in high-resolution spectral protocols.\u003c/p\u003e \u003cp\u003eThese findings reinforce the importance of aligning the choice of spectroscopic technique with the morphological characteristics of the sample. Although most spectra did not exhibit classical degradation bands typically associated with oxidation (1700–1740 cm⁻¹) or hydrolysis (3200–3600 cm⁻¹), localized spectral alterations were observed in PMMA, PU, PET, and PP, suggesting the onset of degradation processes. These subtle changes are illustrated in the interferograms provided in the supplementary material (see interferogram in Supplementary Material).\u003c/p\u003e \u003cp\u003eThe comparative analysis revealed that ATR-FTIR consistently achieved superior performance in terms of spectral match quality. Mean HQI values exceeded 90% for polymers such as PET, ABS, and EVA, corroborating previous studies that support the robustness and reliability of the ATR method (Sorasan et al. \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Conterosito et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Romeo \u0026amp; Diem \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Bassan et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). In contrast, µFTIR showed limited performance when applied to particles ≥ 500 µm, in both transmission (T500) and reflection (R500) modes. HQI values for PET, PS, epoxy resin, and PP often fell below 70%, likely due to optical artifacts resulting from excessive particle thickness, surface irregularities, or even early signs of degradation, as observed in the case of PET. These results highlight the need for optimized sample preparation and methodological adaptation when analyzing heterogeneous or weathered plastic fragments using µFTIR.\u003c/p\u003e \u003cp\u003eReducing the particle size to 100 µm (T100, R100) substantially improved µFTIR spectral quality, with HQI values exceeding 90% for PS, PET, and epoxy resin in both modes. This confirms particle size as a critical factor for spectral accuracy in µFTIR, since smaller particles enhance optical homogeneity and minimize scattering and diffuse reflection (Sorasan et al. \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Campanale et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAlthough the reflectance mode in µFTIR is susceptible to optical artifacts and spectral distortions, it offers benefits such as reduced data acquisition time and higher throughput in environmental analyses. Visual overlay facilitated peak matching assessment, providing qualitative support for polymer identification, with particular attention to the fingerprint region (1850–700 cm⁻¹), recognized as the most specific and informative (Cowger et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Even though reflectance spectra are harder to interpret than ATR-FTIR spectra, they still provide chemically relevant information, allowing identification of key absorption regions such as CH stretching (3000–2800 cm⁻¹), carbonyl (1820–1680 cm⁻¹), and NH stretching (3500–3300 cm⁻¹). These data support the exploratory potential of reflectance-FTIR, especially when supplemented by multivariate methods for interpretation (Xu et al. \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAmong the twelve polymers analyzed, most exhibited well-preserved spectra comparable to virgin materials. Nylon showed stable bands—including N–H stretching (~ 3298 cm⁻¹), C = O (~ 1634 cm⁻¹), and multiple CH₂ bending vibrations—but presented minor variations attributed to moisture absorption (Noda et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Jung et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). In epoxy resin, spectral comparison revealed clear evidence of oxidative and photochemical degradation, indicated by an increased C = O (~ 1730 cm⁻¹), a reduced aromatic C = C (~ 1600 cm⁻¹), and an increased O–H (~ 3400 cm⁻¹), suggesting formation of carbonyl groups, partial aromatic ring cleavage, and ether network scission (González et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe FTIR spectrum of the polycarbonate (PC) sample displayed characteristic bands, including C = O (~ 1780 cm⁻¹), aromatic C = C (~ 1600 cm⁻¹), aromatic C–H (~ 2972 cm⁻¹), and CH₃ (~ 1405 cm⁻¹). However, a reduced intensity of the C = O band (~ 1780 cm⁻¹) was observed, indicating oxidative degradation of the material (Wang et al. \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTransmission mode produced high-quality spectra for thin particles, but thick (\u0026gt; 100 µm) or pigmented particles caused total IR beam absorption, compromising detection of absorption bands and leading to unidentifiable spectra. This reinforces the need to select acquisition mode based on sample and substrate properties, as unsuitable substrates may further degrade signal quality (Sorasan et al. \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Background spectra are essential to correct noise from water vapor, CO₂, or substrates, preventing misinterpretation from environmental or material interferences.\u003c/p\u003e \u003cp\u003eµFTIR coupled with optical microscopy inherently has a spatial resolution limit (~ 10 µm), defined by the diffraction limit and Rayleigh’s criterion, influenced by the objective lens’s numerical aperture. The choice between reflectance or transmission mode in µFTIR depends on the substrate used for particle deposition, directly affecting spectral quality and feasibility (Sorasan et al. \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). These results highlight methodological limitations in extracting chemical information solely from spectral data, which can hinder accurate polymer classification—particularly in environmentally weathered samples. However, such limitations can be mitigated through the integration of spectroscopic data with multivariate analytical approaches (e.g., PCA, Random Forest), which enhance classification accuracy and robustness (Silva et al. \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Liu et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Villegas-Camacho et al. \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Conterosito et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Analytical grouping of polymers by HQI behavior\u003c/h2\u003e \u003cp\u003eBased on HQI values across techniques and sizes, polymers were grouped into three categories:\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eGroup 1\u003c/b\u003e: ABS, PMMA, and PVC – high consistency and low CV (\u0026lt; 10%), reflecting stable performance.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eGroup 2\u003c/b\u003e: PE, PP, PS, PET, EVA, and epoxy resin – strong improvement in 100 µm analysis; highly affected by size and acquisition mode.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eGroup 3\u003c/b\u003e: Nylon, PU, and PC – best performance under ATR; µFTIR results were variable, particularly at 500 µm.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003ePolymers were grouped based on spectral performance. The first group—ABS, PMMA, and PVC—showed high consistency, with mean HQI values above 80% and coefficients of variation (CV) below 10% across techniques and particle sizes. ABS stood out, achieving a mean HQI of 86.6% and a CV of 7.6%, reflecting high reproducibility, likely due to favorable optical properties such as low surface roughness, controlled opacity, and homogeneous chemical composition (Rathore et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Cowger et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe second group—PE, PP, PS, PET, EVA, and epoxy resin—performed better at 100 µm, particularly under µFTIR. PS, for example, reached an HQI of 62.5% at R500 but improved to 94.5% at R100. Epoxy resin showed the lowest individual HQI (56.2% at R500) but achieved satisfactory values in T100 (93.7%) and R100 (91.1%). These polymers were more sensitive to analytical conditions, with higher CVs (e.g., PET: 21.9%; PS: 16.5%; epoxy resin: 16.9%).\u003c/p\u003e \u003cp\u003ePET exhibited substantial variation across spectroscopic techniques, highlighting the influence of acquisition mode and particle size on identification. Although it achieved 97.2% HQI in ATR, PET had unsatisfactory values in T500 (59.4%) and R500 (59.5%), improving at T100 (82.8%). Spectra showed broadening of the O–H band (~ 3300 cm⁻¹), carbonyl changes (~ 1720 cm⁻¹), and loss of definition in 1500–900 cm⁻¹, suggesting mild degradation from oxidation or hydrolysis (Sammon et al. 2000; Du et al. 2014).\u003c/p\u003e \u003cp\u003eThe third group—nylon, PU, and PC—performed better under ATR, with lower HQIs and greater variability in µFTIR, especially reflection at 500 µm. PU showed an HQI of 80% in ATR but dropped to 69.2% in R500, possibly reflecting chemical degradation. Spectral changes included intensified carbonyl (~ 1720 cm⁻¹) and attenuated N–H (~ 1530 cm⁻¹), indicating oxidation and urethane chain hydrolysis (Jung et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Noda et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2007\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePolycarbonate (PC) achieved HQI values from 70.5–83.1%, ensuring satisfactory identification without exceptional performance. Factors such as multilayer structure, pigments, and surface impurities likely reduced spectral quality (Willans et al. \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Preliminary analysis yielded HQI \u0026lt; 60% for PC; however, reanalysis with another particle surpassed the acceptance threshold, illustrating intra-sample heterogeneity’s impact on result variability (Käppler et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Silva et al. \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThese results show that while spectra retained sufficient markers for identification, oxidative degradation, hydrolysis, or photo-oxidation introduced structural changes, potentially reducing spectral similarity with virgin polymer standards. These chemical alterations (e.g., formation of carbonyl and hydroxyl groups) increase chemical and physical heterogeneity, leading to new bands, broadening, reduced peak definition, and decreased relative intensities—compromising HQI values, especially in µFTIR. Additionally, these modifications amplify scattering and diffuse reflection in reflectance mode or cause total absorption in transmission mode, resulting in masked or distorted spectra. These findings emphasize the need for spectral libraries representing environmentally degraded polymers and complementary multivariate approaches for accurate identification.\u003c/p\u003e \u003cp\u003eOverall, most polymers retained spectra comparable to virgin materials. However, alterations from oxidative degradation, moisture absorption, and photochemical processes hindered precise identification. These variations are especially relevant for environmental samples exposed to weathering, biofilm formation, and contaminant adsorption (Lee et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Ivleva \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Integrating multiple analytical modes helps overcome limitations from heterogeneous surfaces and morphological irregularities (Tanoiri et al. \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Rathore et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn this context, coupling µFTIR with machine learning (ML) algorithms offers promising improvements in spectral classification, especially for degraded environmental samples. PCA, Random Forest, and SVM approaches have enhanced automated classification and mitigated baseline distortions and pigment interference (Silva et al. \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Liu et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Villegas-Camacho et al. \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Confirmatory techniques such as Raman microspectroscopy, DRIFTS, or Py-GC-MS can further increase analytical robustness under real-world conditions (Cowger et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Primpke et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Primpke et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis study presents experimental evidence of the limitations of µFTIR for analyzing microplastic particles ≥ 500 µm. The results show a significant decrease in HQI values for PET, PS, PP, and epoxy resin compared to ATR-FTIR, reinforcing the latter's superior reproducibility and spectral fidelity for thick or morphologically complex particles. These findings help address gaps in ISO 24187:2023 and ASTM D8333-20, which acknowledge the influence of morphology but do not establish quantitative spectral quality criteria or clear particle size thresholds.\u003c/p\u003e \u003cp\u003eOur data support adopting HQI ≥ 80% as a minimum validation criterion for µFTIR reflectance analysis of 100 µm particles, contributing to the standardization of analytical protocols for environmental microplastic monitoring. Although the standards recommend adapting the acquisition mode (transmission, reflection, or ATR) to the physical and chemical properties of the sampl - such as thickness, roughness, and pigmentation- they do not specify ideal particle size ranges or corrective measures for thick particles. Section 6.4.2 of ISO 24187:2023, for instance, advises choosing the acquisition mode based on morphology but lacks objective operational parameters. This study helps fill that gap by empirically demonstrating the reduced reliability of µFTIR for particles ≥ 500 µm, as evidenced by HQI values frequently falling below the acceptance threshold (≥ 70%).\u003c/p\u003e \u003cp\u003eWhile this guidance recognizes the influence of thickness and morphology on analysis, the standard does not define an ideal particle size range for µFTIR use, nor does it suggest corrective measures for thick particles (\u0026gt; 300–500 µm). Therefore, this study addresses this practical gap by empirically demonstrating that µFTIR loses reliability for particles ≥ 500 µm, as evidenced by HQI values falling below the automatic acceptance threshold (≥ 70%). Similarly, ASTM D8333-20, which focuses primarily on sample preparation (chemical and enzymatic digestion), does not discuss the impacts of thickness or roughness on spectral quality, nor does it provide recommendations for selecting the optimal acquisition mode based on particle size. The standard assumes that, once organic interferents have been removed, the sample is ready for FTIR or Raman analysis, without addressing the inherent optical limitations of µFTIR when applied to large, heterogeneous particles.\u003c/p\u003e \u003cp\u003eThese results underscore the need for more explicit quantitative guidelines in international standards, particularly regarding the application of µFTIR to environmentally relevant, morphologically heterogeneous microplastics. By offering experimental evidence and HQI-based acceptance parameters, this study contributes to ongoing efforts toward methodological harmonization and to improving analytical practices for environmental microplastic monitoring.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.4. Practical implications and methodological guidance\u003c/h2\u003e \u003cp\u003eOur results confirm that µFTIR is susceptible to spectral distortions in large or morphologically complex particles, particularly in transmission mode. Small particles (≤ 100 µm) offer superior resolution and lower variability, supporting their use in standard protocols. Despite µFTIR’s sensitivity to optical artifacts, its high spatial resolution and ability to detect local compositional changes remain valuable—particularly for weathered, pigmented, or biofilm-coated microplastics (Shi et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Combining µFTIR with multivariate tools (e.g., PCA, Random Forest) and complementary methods (e.g., DRIFTS, Raman, Py-GC-MS) can enhance accuracy under environmental conditions (Liu et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Conterosito et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBy integrating spectral, morphological, and statistical analyses, this study offers quantitative evidence to support HQI-based decision criteria and promotes methodological harmonization in microplastic identification.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study demonstrated that both FTIR acquisition mode and particle size are critical factors in the spectral identification of fragmented microplastics. ATR-FTIR consistently delivered high HQI values for particles ≥ 500 µm, particularly for PET, ABS, and EVA, confirming its robustness for larger, thicker fragments. In contrast, µFTIR showed reduced spectral performance with larger particles, while analysis of 100 µm fragments, especially in reflection mode (R100), achieved HQI values comparable to ATR-FTIR. Additionally, the use of ZnSe windows proved effective for µFTIR analysis, ensuring high spectral transmission and reproducibility even with smaller particles—an aspect still scarcely reported in the literature and which reinforces their potential for broader adoption in standardized protocols.\u003c/p\u003e\u003cp\u003eThese findings underscore the need to align analytical strategies with the morphological and physical properties of microplastics to ensure accurate polymer identification. Furthermore, they highlight the importance of methodological standardization—including optimal particle size selection, acquisition mode, and optical components—to improve reproducibility and comparability across studies. This work offers practical insights to refine analytical protocols and supports ongoing efforts to harmonize microplastic monitoring methodologies in environmental research, ultimately enabling more accurate and reliable assessments of microplastic pollution across diverse environmental matrices.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank the Rio de Janeiro State Research Support Foundation (FAPERJ) for financial support through research grants under process numbers E-26/203.421/2023, E-26/210.769/2021, and E-26/201.111/2021.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank the Rio de Janeiro State Research Support Foundation (FAPERJ) for financial support through research grants under process numbers E-26/203.421/2023, E-26/210.769/2021, and E-26/201.111/2021.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBarbara Rodrigues Geraldino: Conceptualization, Formal analysis, Investigation, Methodology, Validation, Writing \u0026ndash; original draft, Writing \u0026ndash; review \u0026amp; editing\u003c/p\u003e\n\u003cp\u003eStella Melga\u0026ccedil;o: Methodology, Writing \u0026ndash; original draft\u003c/p\u003e\n\u003cp\u003eDaniele Bila Maia: Supervision, Validation, Writing \u0026ndash; review \u0026amp; editing\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of Competing Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Approval*\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis is not applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Participate*\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis is not applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Publish*\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis is not applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data supporting the findings of this study are included in this article and its supplementary information files.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAlberghini L, Truant A, Santonicola S, Colavita G, Giaccone V (2022) Microplastics in fish and fishery products and risks for human health: A review. 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Science of The Total Environment 837:155719. https://doi.org/10.1016/j.scitotenv.2022.155719\u003c/li\u003e\n\u003cli\u003eWillans M, Szczecinski E, Roocke C, Williams S, Timalsina S, Vongsvivut J, McIlwain J, Naderi G, Linge KL, Hackett MJ (2023) Development of a rapid detection protocol for microplastics using reflectance-FTIR spectroscopic imaging and multivariate classification. Environ Sci Adv 2(4):663\u0026ndash;674. https://doi.org/10.1039/D2VA00313A\u003c/li\u003e\n\u003cli\u003eXu J-L, Hassell\u0026ouml;v M, Yu K, Gowen AA (2020) Microplastic Characterization by Infrared Spectroscopy. In: Rocha-Santos T, Costa M, Mouneyrac C (eds) Handbook of Microplastics in the Environment. Springer International Publishing, Cham, pp 1\u0026ndash;33\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"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":"Microplastics, ATR-FTIR, µFTIR, Hit Quality Index, Polymer identification, Environmental monitoring","lastPublishedDoi":"10.21203/rs.3.rs-6683637/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6683637/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study systematically evaluated the performance of Attenuated Total Reflectance (ATR) and micro-Fourier Transform Infrared Spectroscopy (\u0026micro;FTIR) in transmission and reflection modes for the identification of microplastics (MPs) from real-world plastic products. Twelve common polymers were analyzed in two particle size ranges (500 \u0026micro;m and 100 \u0026micro;m). Identification quality was assessed using the Hit Quality Index (HQI), with a validation threshold of \u0026ge;\u0026thinsp;70%. ATR analysis of 500 \u0026micro;m particles yielded HQI values\u0026thinsp;\u0026gt;\u0026thinsp;80% for all polymers, with PET (97.2%), ABS (93.2%), and EVA (92.3%) achieving\u0026thinsp;\u0026gt;\u0026thinsp;90%, demonstrating high spectral fidelity and reproducibility. \u0026micro;FTIR exhibited significant size-dependent variation: for 100 \u0026micro;m particles, reflection mode (R100) achieved HQI values\u0026thinsp;\u0026gt;\u0026thinsp;85% for most polymers, including 94.5% for PS and 93.7% for epoxy resin. Conversely, \u0026micro;FTIR performance declined for 500 \u0026micro;m particles, with HQI values\u0026thinsp;\u0026lt;\u0026thinsp;70% for PET, PS, epoxy resin, and PP. ANOVA (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) and Tukey\u0026rsquo;s post hoc test confirmed significant differences across techniques and particle sizes, with R100 performing comparably to ATR. These results highlight the influence of particle morphology and acquisition mode on spectral identification and emphasize the need for harmonized analytical protocols. These findings contribute empirical support for the refinement of current standards (e.g., ASTM D8333-20, ISO 24187:2023) recommending HQI\u0026thinsp;\u0026ge;\u0026thinsp;80% as a reliable threshold for polymer identification via \u0026micro;FTIR.\u003c/p\u003e","manuscriptTitle":"Harmonizing infrared spectroscopic techniques for microplastic identification: a comparative evaluation of ATR and µFTIR transmission and reflection modes","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-09 05:23:16","doi":"10.21203/rs.3.rs-6683637/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":"acd874f9-da7c-4784-9821-402a7c7e4f7e","owner":[],"postedDate":"June 9th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-07-09T08:04:09+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-09 05:23:16","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6683637","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6683637","identity":"rs-6683637","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}