Understanding your biases in collecting organismal VOCs

Understanding your biases in collecting organismal VOCs | 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 Understanding your biases in collecting organismal VOCs Lucas Seybert, Christophe Duplais This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5462922/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 14 Mar, 2025 Read the published version in Journal of Chemical Ecology → Version 1 posted 8 You are reading this latest preprint version Abstract Volatile organic compounds (VOCs) play a fundamental role in organismal interactions, facilitating intra- and interspecific communication. Accurate collection and analysis of VOCs are essential for understanding these interactions, but the choice of collection method and adsorbent material can introduce biases. This study investigates the variability and recovery yield in VOC collection using various adsorbents and thin-film solid-phase microextraction (TF-SPME). We compared the performance of nine adsorbents and TF-SPME in capturing a standard VOC mixture and VOCs from rosemary plants. Results show significant differences in efficiency among adsorbents, with Porapak® P generally providing the best recovery for most compounds. TF-SPME exhibited higher sensitivity and detected a broader range of volatiles compared to adsorbents, though qualitative profiles varied. Our findings underscore the importance of empirical testing in adsorbent selection and highlight the inherent biases in VOC collection methods. These insights aim to guide and empower researchers in making informed decisions regarding experimental design and data interpretation to achieve more accurate and reliable VOC results in chemical ecology studies. VOC SPME adsorbents headspace Figures Figure 1 Figure 2 Figure 3 INTRODUCTION Volatile organic compounds (VOCs) emitted by organisms play a fundamental role in biological interactions. These chemical molecules, released by an emitter, can be detected by a receiver, thus facilitating communication both within and between species (Knudsen and Gershenzon 2020 ). VOCs are involved in processes such as plant-plant communication (Bouwmeester et al. 2019 ), plant-insect interactions (Zhou and Jander 2022), insect communications (Ali and Morgan 1990 : Renou 2014 ; Renou and Anton 2020 ), tri-trophic interactions (Turlings and Erb 2018 ), plant-microbe interactions (Fincheira et al. 2021 ; Sharifi et al. 2022 ) and microbe communications (Weisskopf et al. 2021 ), significantly influencing ecological dynamics and evolutionary trajectories (Pichersky and Gershenzon, 2002 ; Dudareva et al. 2006 ; Heil and Karban 2010 ). VOC research also has practical applications: for instance, plants infected by viruses (Mann et al. 2012 ; Paris et al. 2024 ) or experiencing abiotic stress conditions (Holopainen and Gershenzon 2010 ; Jin et al. 2023 ) exhibit distinct VOC signatures, which can serve as potential markers for assessing plant health. For all these reasons, accurate collection and analysis of VOCs are paramount for advancing our understanding of these complex biological interactions and environmental adaptations. VOC collection methods can be divided into two broad categories: static and dynamic (Tholl et al. 2021 ). Static collection, which often involves the use of solid-phase microextraction (SPME), allows the passive adsorption of VOCs onto a solid adsorbent matrix. Headspace SPME is based on the partition equilibrium of analytes between the sample matrix and the atmosphere followed by the thermal desorption (TD) of analytes (Bruheim et al. 2003 ; Dugheri et al. 2022 ). The first SPME device was an adsorbent fiber coated with polydimethylsiloxane (PDMS) (Pawliszyn 1997 ), with other adsorbent polymers subsequently becoming available. This method is valued for its simplicity and minimal equipment requirements but has limitations, such as the need for immediate analysis after VOC collection and difficulty in obtaining a high number of replicates due to the lack of GC autosampling for SPME fibers. The development of a thin film (TF) SPME, with a significantly higher surface area than traditional fiber SPME, combined with automated TD, has circumvented some of these drawbacks, and its increased sensitivity and relative ease of handling have expanded applications (Emmons et al. 2019 ). However, TF-SPME requires additional gas chromatography (GC) equipment like headspace autosamplers and thermal desorption units. All SPME technologies have the additional drawback that once VOCs are desorbed individual samples cannot be re-analyzed. Dynamic collection methods, such as the use of adsorbent tubes coupled to an air pump, actively draw air through an adsorbent material, resulting in the accumulation of VOCs (Helmig and Greenberg 1994 ). This method has historically played an important role in chemical ecology and the very first article in the Journal of Chemical Ecology reports the use of Porapak® Q to collect insect pheromones (Byrne et al. 1975 ). The major advantage of dynamic collection is the ability to use solvent extraction, which facilitates the purification of large quantities of VOCs, allows for absolute quantification using external or internal standards, and produces samples that may be re-analyzed multiple times if necessary. Additionally, novel VOCs can be structurally characterized by trapping and purifying large quantities, such as in the case of the striped cucumber beetle ( Acalymma vittatum ) aggregation pheromone vittatalactone (Morris et al. 2005 ). The choice of adsorbent material significantly impacts the efficiency and reliability of VOC collection. Different adsorbents vary in their affinity for specific compounds, recovery rates, and susceptibility to saturation and contamination (Nongonierma et al. 2006 ). Popular sorbents include carbon-based matrices (activated charcoal, graphite, molecular sieves) and synthetic polymers, which are available either as raw materials or pre-packed in sealed tubes. Navigating the chemical compositions of adsorbents can be challenging for non-chemists, especially when sold under brand names like the Orbo®, Hayesep® and Porapak® series. The specific polarity, surface area, and pore size of these adsorbents can affect their efficiency in collecting VOCs (Xie et al. 2022 ), introducing additional biases. Other confounding factors may arise from compounds reacting with the adsorbent matrix, such as when ocimene reacts with activated charcoal to give the oxidation products octatetraene and octatrienol (Brunke et al. 1993 ). Finally, another bias related to environmental conditions is the degradation of certain terpenes in the presence of trace amounts of ozone (Calogirou et al. 1996 ; Pinto et al. 2007 ). Understanding these biases is crucial for selecting the appropriate collection method and adsorbent for specific ecological studies. Different adsorbents can lead to different chemical profile results, as described for floral VOCs using activated charcoal and Tenax® (Brunke et al. 1994 ), but an updated comparison of several commercially available adsorbents has not been conducted recently. This paper investigates how VOC collection methods, in particular adsorbent choice, affects variability and recovery of VOCs, and aims to provide a comprehensive assessment of biases in VOC collection for studies in chemical ecology. We used both a synthetic mixture of VOCs and VOCs collected from rosemary ( Rosemarinus officinalis ) plants to compare the performance of different absorbents, and the variation of chemical diversity compared to TF-SPME. VOC analysis, regardless of the method used, reflects only part of the qualitative and quantitative reality of VOCs emitted by organisms; this study serves as not only a reminder of methodological shortcomings, but as a framework for understanding how VOC collection methods bias our understanding of the systems we study. METHODS AND MATERIALS Chemicals, Sorbent Tubes, and TF-SPME. HPLC-grade methyl acetate was purchased from MilliporeSigma (Saint Louis, MO). Linalool oxides, n-hexanal, and 2-phenylethyl alcohol were purchased from TCI (Tokyo, Japan). ( R )-(+)-limonene, cis-3-hexenyl acetate, (-)-linalool, and n-pentadecane were purchased from MilliporeSigma (Saint Louis, MO). Methyl salicylate was purchased from Chem-Impex (Wood Dale, IL). β-caryophyllene was purchased from ICN Biomedical (Costa Mesa, CA). Pre-packed sorbent tubes were obtained from the following vendors: Porapak® P (Divinylbenzene (DVB)/Styrene, ZST-125), Porapak® R (DVB/Vinyl methylpyrrolidone, ZST-135), and Porapak® N (DVB-ethylvinylbenzene-ethyleneglycol dimethacrylate, ZST-120) tubes were purchased from Zefon International; Orbo® 32 (activated charcoal), Orbo® 47 (Supelpak® 70 = Amberlite XAD-7®, aliphatic acrylic polymer), Orbo® 90 (Carboxen-564, carbon molecular sieves), and Orbo® 101 (carbotrap, graphitized carbon black) tubes were purchased from MilliporeSigma; Tenax® (SKC 226-35-03) tubes were purchased from SKC Inc. Porapak® Q (DVB-ethylvinylbenzene) were purchased from MilliporeSigma (Orbo® 1103), Zefon International (ZST-130), and SKC Inc. (SKC 226 − 115). (Note that Super Q is Porapak® Q extensively washed with organic solvents and acids to remove residual monomers and impurities.) The adsorbents are conditioned by eluting 2 mL of methyl acetate to remove impurities and then dried at 50°C (120°F) overnight except for Tenax® and Orbo® 32 which did not have significative contmination. Table S1 summarizes the characteristics of absorbents (not only those used in this study) in terms of chemical composition, polarity, pore diameter, and surface area, and provides reference codes from different vendors. Polydimethylsiloxane (PDMS)-DVB thin-film solid phase microextraction (TF-SPME) devices were purchased from Gerstel (Gerstel GmbH, Müllheim an der Ruhr, Germany) and conditioned at 250°C for one hour prior to use. Preparation of the VOC Standard and Desorption Solvent. A 1 mg/mL solution of n-hexanol, n-hexanal, cis-3-hexenyl acetate, (R)-(+)-limonene, linalool oxide (1:1 mix of isomers), (-)-linalool, 2-phenylethyl alcohol, methyl salicylate, and β-caryophyllene was made using methyl acetate as a solvent. This mixture was used to prepare the VOC standard curve as well as the 250 µg/mL mixture used in the VOC standard trials. 1, 10, 50, and 100 µg/mL calibration standards, all containing 52.6 µg/mL n-pentadecane as an internal standard, were prepared from the VOC standard mixture. VOC Standard Trials. Holes were drilled in the lids of 480 mL glass jars (Qorpak®) and ¼ inch polypropylene compression fittings (JACO Manufacturing, Berea, OH) were inserted. To each jar was attached a portable air pump (Pocket Pump Touch, SKC) and a pre-packed sorbent tube. 40 µL of 250 µg/mL VOC standard mixture was spotted onto filter paper, which was placed in the bottom of each jar. The lids were closed tightly, and the mixture was allowed to evaporate for 20 minutes. Then air was pumped through the jars at 500 mL/min for 3h before the sorbent tubes were removed and promptly desorbed by loading desorption solvent (methyl acetate containing 52.6 µg/mL n-pentadecane as an internal standard) onto the tube and allowing it to drip through until the solvent filled a 250 µL vial insert. In addition, 40 µL of 250 µg/mL VOC standard was diluted to the same total volume using desorption solvent and the resulting samples (N = 3) were run in parallel to the adsorbent samples in order to determine the recovery yield. Live Plant Trials. Rosemary ( Salvia rosmarinus ) plants were purchased from a local garden center. Branches weighing 0.9–1.5 g were removed from the plants and cut into three segments. These segments were enclosed in jars fitted with pumps and sorbent tubes, as described above, and allowed to equilibrate for 20 minutes before air was pumped through the system at 500 mL/min for 3 hours. The sorbent tubes were desorbed with HPLC-grade methyl acetate as described previously, and the resulting samples were spiked with 5 µL of a standard solution of 1.32 mg/mL n-pentadecane in methyl acetate. In parallel, cut rosemary segments were sealed in jars in which a TF-SPME device had been suspended, and the devices were allowed to equilibrate for 3 hours and 20 minutes before they were removed and analyzed via GC-MS. Analysis by GC-MS. All samples were analyzed using an Agilent 7890B GC coupled with a 5977C MS (Agilent Technologies, Santa Clara, CA). The instrument was fitted with a CIS 4 inlet connected to either a septumless sampling head for liquid injection or TD3.5 + thermal desorption unit (Gerstel GmbH, Müllheim an der Ruhr, Germany). Liquid samples were analyzed using the following instrument parameters: 1 µL of liquid sample was injected at a 10:1 split (for the VOC standard trials) or in splitless mode (for the rosemary trials) onto a 60 m x 0.25 mm x 0.25 µm HP-5MS column (Agilent Technologies) with a notched glass inlet liner (Gerstel) at a constant He flow of 1.2 mL/min. The inlet was held at 40°C for 0.5 min and then ramped at 12°C/s to a final temperature of 280°C, where it was held for the remainder of the run. The GC oven program began at 45°C, then was ramped according to the following protocol: 10°C/min to 100°C; 3°C/min to 130°C; 10°C/min to 180°C; and finally 30°C/min to 280°C, where it was held for 5 minutes. The MS was operated in scan mode with a m/z range of 29–550 and a solvent delay of 5.5 minutes. TF-SPME samples were desorbed at 250°C for 5 minutes while the inlet was held at -35°C, and then the inlet was heated at 12°C/s to a final temperature of 270°C. A deactivated glass wool inlet liner was used. All other GC/MS parameters were identical to the method used for the plant adsorbent trials, except that the solvent delay was reduced to 1 minute. Data Analysis. Agilent MassHunter Quantitative Analysis software was used to analyze data from the VOC standard trials. Recovery of all VOCs was quantified based on a calibration curve and recovery of the internal standard. Significant suppression (≥ 50%) of the internal standard was noted in Orbo® 32, 90, and 101 samples, so internal standard recovery was disregarded in the analysis of those trials. The resulting concentration of each VOC was expressed as a percent recovery compared to the amount obtained when spiking VOC mixture directly into the vial. Open-source software MZmine 4.0.8 was used to identify the primary volatile components in the rosemary adsorbent and TF-SPME samples. The MZmine processing wizard was configured for low resolution GC-EI data in the processing wizard batch configuration and the following custom parameters were entered: a noise threshold and minimum feature height of 150, m/z tolerance of 0.1, and a minimum of three signals in the deconvoluted spectrum. Samples were also analyzed in the Agilent Unknowns Analysis software using the 2020 NIST mass spectral library to tentatively identify features. The feature list generated by MZmine was manually compared to the output from Unknowns Analysis in order to annotate features. In the adsorbent samples, all compounds appearing in two or more samples from a single treatment were annotated, and a total of nine biogenic VOCs were identified as compounds of interest. Due to the large number (95) of features detected in TF-SPME samples compared to the low number (< 20) of features detected by all sorbent tubes, annotation was limited to the twenty features with the highest average recovery. Features that were identified as probable contaminants (e.g. breakdown products of polymers) were disregarded. The annotation was compared to documented retention indices of terpenes (Babushok et al. 2011 ) and previously characterized volatile components of rosemary (Verma et al. 2020 ) to corroborate the identities of each feature. The list of ten VOCs with the highest average recovery in the TF-SPME trials was consolidated with the list of nine VOCs identified in the adsorbent samples, and a total of eleven VOCs were identified as compounds of interest for the purposes of comparing adsorbents and TF-SPME. MassHunter Quantitative Analysis was used to calculate the area of the base peak of each compound, which was used as a proxy for concentration in the ensuing statistical analysis. As TF-SPME and adsorbent samples were run using different GC methods, area counts could not be compared directly; instead, the area of each compound was expressed as a percentage of the summed area of all peaks. Statistical Analysis. Statistical analyses were performed in R (v. 4.3.3) using RStudio (2024.09.1) as an environment. Analysis of variance was performed individually for each compound, with adsorbent as the treatment and adjusted area (or calculated concentration) as the dependent variable. Logarithmic scaling of the data or a Welch’s ANOVA was performed in cases where the data set violated the assumptions of a traditional ANOVA. Tukey’s HSD ( P < 0.05) was used to identify which treatments differed significantly from one another. Additional details of the statistical analyses may be found in the Supplementary Information. RESULTS As expected, different adsorbents recovered significantly different quantities of VOCs from an artificial VOC blend. (Fig. 1 ). Porapak® P had the highest average recovery yield for all compounds except hexanal and exhibited relative standard errors (RSE) below 5% for most compounds. Orbo® 101 and Orbo® 47 also performed well, with > 50% average recovery yield for most compounds. Although there is no statistically significant difference between the recovery yields from Orbo® 101 and Orbo® 47, Orbo® 47 had lower relative standard error: the RSE for all compounds except methyl salicylate was < 5% in Orbo® 47 but ranged from 7.7–12.4% with Orbo® 101. Porapak® N and Porapak® R had similar average recovery yields to Orbo® 47, but high variability, with RSEs exceeding 20% for many compounds. Orbo® 32 performed comparably to Orbo® 47 for most compounds, but had lower average recovery yields of R -(+)-limonene and methyl salicylate. Tenax® had excellent reproducibility, with RSEs as low as 2%, but recovery was strongly dependent on VOC. 3-Hexenyl acetate and methyl salicylate were recovered as effectively by Tenax® as by Porapak® P, while hexanal and linalool oxides had average recovery yield of 6.3% and 4.0%, respectively, comparable with the worst-performing adsorbents. Porapak® Q produced average recovery yields between 19% and 43%, and despite having double the number of replicates of any other adsorbent tested (N = 12 versus N = 5–6), RSEs were still between 20% and 30% for most compounds. Only Orbo® 90 yielded lower average recovery than Porapak® Q, with no more than 25% of any compound recovered and RSEs between 20% and 25%. Table S3 details the mean recovery and standard error (SE) for every combination of compound and adsorbent, and also shows which differences between adsorbents are statistically significant (Tukey’s HSD, P < 0.05). For every compound tested, Porapak® P either had the highest average recovery, or was not significantly different from the adsorbent with the highest average recovery. There was no clear correlation between the recovery yield and material properties such as pore size, polarity, or surface area. Suppression of the internal standard, n-pentadecane, was observed in all three carbon-based adsorbents (Orbo® 32, 90, and 101): the internal standard recovery was approximately 50% in Orbo® 90 and 101, and just 1%-3% in Orbo® 32. These trends were consistent and repeatable across multiple calibrations, and no significant suppression was noted in other adsorbents. Pre-packed sorbent tubes containing Porapak® Q were purchased from three different vendors (SKC, Zefon, and MilliporeSigma) to determine whether the source of the product had an impact on performance. Though qualitative differences were observed between the three vendors (Fig. S1 a), with the tubes from SKC generally having the highest recovery and the tubes from MilliporeSigma generally having the lowest, the variability within vendors was high enough that there was no significant difference between the three. However, significant differences in recovery were found between two different lots of tubes purchased from MilliporeSigma (Fig. S1 b). Recovery data from both lots of MilliporeSigma tubes were included in Fig. 1 . Among the nine adsorbents tested, four were selected for the plant VOCs experiment: Porapak ® P, Porapak ® Q, Tenax®, and Orbo ® 32. Untargeted analysis was performed in two steps: chemical features were identified using MZmine, then those features were annotated using MassHunter Unknowns Analysis. The list of VOCs identified using Porapak ® P and Porapak ® Q was almost identical: α-pinene, β-pinene, camphene, camphor, bornyl acetate, 1,8-cineole, ( R )-(+)-limonene, and terpinolene were detected in both sets of samples, and β-myrcene was detected using Porapak ® P. In contrast, only bornyl acetate, ( R )-(+)-limonene, β-myrcene, and terpinolene were detected in the Tenax ® samples. Orbo ® 32 was ultimately removed from the analysis because no compounds were detected in the original experiment (N = 3) nor in the additional experiment (N = 5) performed to confirm the result was not due to experimental error. Because all samples were collected and run under identical conditions, the base peak area count of a given compound may be treated as a proxy for concentration (Fig. 2 ). Despite the fact that untargeted analysis of Porapak ® P and Porapak ® Q yielded a very similar list of compounds, the targeted quantitative analysis showed that significantly more β-myrcene and ( R )-(+)-limonene were collected by Porapak ® P than Porapak ® Q. Although the concentrations of α-pinene, camphene, and β-pinene were higher in Porapak ® Q compared to Porapak ® P samples, this result was not statistically significant (Fig. 2 ). Tenax ® performed significantly more poorly than either Porapak ® P or Porapak ® Q: α-pinene, camphor, and endo-borneol were present in extremely low quantities in just one sample out of three, and camphene, β-pinene, and 1,8-cineole could not be detected at all. Under similar experimental conditions, TF-SPME detected more compounds (Table S4) in greater quantities: TF-SPME peak areas were orders of magnitude higher, with some peaks showing evidence of detector saturation (Fig. S2). A total of 95 compounds were detected using TF-SPME during untargeted analysis, compared to 14 peaks detected with Porapak® P and 8 peaks detected with Porapak® Q and Tenax®. Though TF-SPME was dramatically more sensitive than any of the adsorbents tested, the list of compounds with the highest area counts was very similar to the list of compounds detected in the adsorbent samples: α-pinene, camphene, camphor, bornyl acetate, 1,8-cineole, ( R )-(+)-limonene, terpinolene, and β-myrcene were found among the top ten peaks with the highest areas. Endo-borneol and γ-terpinene were also in the top ten peaks; β-pinene was present in the TF-SPME samples but was not in the top ten peaks. Targeted analysis of the adsorbent samples detected γ-terpinene in all three adsorbents, and endo-borneol in Porapak® P and Porapak® Q, even though these compounds were not identified during untargeted analysis. This merged list of 11 compounds detected in Porapak® P, Porapak® Q, Tenax®, and TF-SPME was used for further qualitative comparison. TF-SPME requires different experimental and analytical conditions, which makes drawing direct comparisons between results obtained with TF-SPME and adsorbents challenging. Considering each peak area as a percentage of the total area of all 11 peaks helps account for the differences in sensitivity between TF-SPME and adsorbents and gives us a way to visualize the overall VOC profile. Our results highlight qualitative differences between the VOC profiles yielded by different methods (Fig. 3 ). Compared to Porapak ® P and TF-SPME, Porapak ® Q has higher relative percentages of α-pinene, camphene, and β-pinene, the three compounds with the shortest retention times. In contrast, TF-SPME has higher relative quantities of camphor, endo-borneol, and bornyl acetate, the compounds with the longest retention times. Porapak ® P falls somewhere between the two. The absence of several compounds in the Tenax ® samples biases the results in favor of the compounds that were retained, though as in the VOC standard trials, there is no obvious chemical or physical trend in which compounds are favored by Tenax ® . DISCUSSION Our data demonstrate that different adsorbents retain volatiles differently. This conclusion is neither novel nor unexpected: adsorbents are widely used for air sampling and have been rigorously tested for that application (Brown and Shirey 2001 ; EPA 1999). However, industrial contaminants tend to be chemically distinct from VOCs produced by organisms, making it challenging to apply these methods to the field of chemical ecology. A few previous studies (Brunke et al. 1994 ; Alborn et al. 2021 ) have sought to compare different VOC collection methods; our research seeks to expand upon and update these findings with a broader range of commercially available adsorbents, and to compare those adsorbents to the relatively new technology of thin film-SPME. Comparing VOC analyses is challenging because of the profound effect methodology can have on the results. A recent study revealed a high degree of biological variability in the plant VOCs reported by different laboratories, even when those laboratories used the same plant material and experimental design (Eckert et al. 2023 ). By testing different adsorbents using an artificial mixture of VOCs under controlled laboratory conditions, we sought to explore the quantitative differences between those adsorbents with minimal confounding variables. The compounds in the VOC mix were carefully selected to represent a range of plant volatiles with different chemical properties and ecological relevance; a complete list of those compounds, including their structure and biological significance, is given in Table S2. In our VOC standard trials, it was evident that different adsorbents retain VOCs differently. Two metrics were used to determine adsorbent performance: total recovery yield and relative standard error. High recovery yield is critical for untargeted analysis to ensure all VOCs can be strongly retained by the adsorbents with minimal loss, whereas low standard error is important for the quantification of VOCs. Porapak® P's superior recovery yield suggests that it may be particularly effective for a broad range of VOCs, whereas the low relative standard error of Tenax® make it an excellent choice for studies requiring consistent results across replicates. In addition to excellent recovery, Porapak® P had below-average standard error, making it a versatile choice but not necessarily the best for every system. Different ecological systems, such as plant, animal, and microbe volatiles, contain diverse types of volatiles with different structures (aliphatic, aromatic, heteroaromatic) and functional groups (hydrocarbon, alcohol, aldehyde, carboxylic acids, esters, ethers, amides, amines, thiols, etc.) The artificial VOC blend used in this study was designed to represent the diversity of plant VOCs, but for a different ecological system, another adsorbent may prove superior to Porapak ® P. Importantly, no clear correlation was identified between the recovery yield and any single material-specific characteristic such as pore size, polarity, or surface area. However, we found that different lots of Porapak® Q sourced from the same supplier have different VOC recovery and variability. This implies that VOC collection efficiency is complex and susceptible to factors as unpredictable as manufacturing inconsistencies, and it underscores the need for empirical testing of adsorbents. In addition, the suppression of the internal standard in Orbo® 101, 90, and 32 illustrates the potential for unexpected interactions between adsorbents, VOCs, and standards. For the second experiment, we chose four adsorbents based on their performance in the VOC standard experiment and their use in chemical ecology studies: Porapak® P, Porapak® Q, Tenax®, and Orbo® 32. The difference between Porapak® P and Q was less striking in the rosemary experiment than in the VOC standard trial, but still present for certain VOCs. In all instances where there was a significant difference between the two, Porapak® P had higher recovery than Porapak® Q. Tenax® showed a similar trend to the first experiment, performing as well as Porapak® P and Q for some VOCs but failing to detect about half, with no clear trends regarding which compounds were lost. Orbo® 32 was unable to detect any compounds, likely due to the retention of molecules within the matrix. Coconut charcoal has been shown to strongly adsorb a range of different VOCs (Brown and Shirey, 2001 ) This is supported by the fact that extremely low levels of the internal standard n-pentadecane were detected in the Orbo® 32 samples during the VOC standard trials, implying that the internal standard in the extraction solvent was retained in the sorbent tube. When comparing the results of the rosemary trials with the VOC standard trials, it is important to keep a few salient differences between the two experiments in mind. Firstly, because the rosemary trials were limited to a specific system, there is a high degree of chemical similarity between the VOCs detected: all are mono– and sesquiterpenes and their derivatives. This is in contrast to the VOC standard trials, in which a much higher degree of chemical diversity was present. Secondly, plant tissue emits VOCs slowly, which results in less opportunity for break-through, where continued airflow causes a weakly adsorbed compound to desorb and be lost to the environment. In the VOC standard experiment, evaporation occurs much more rapidly, potentially increasing the rate of breakthrough. This raises one important variable in VOC collection that this study does not explore: air volume. Collection time and flow rate both impact the total volume of air that passes through the adsorbent tube, which in turn impacts the rate of break-through (Brown and Shirey 2001 ). Dynamic collection methods using adsorbents can provide detailed VOC profiles but may miss certain compounds which TF-SPME is capable of detecting. TF-SPME detected a higher number of volatiles and showed evidence of detector saturation, indicating that TF-SPME is a more sensitive method for VOC detection. Drawing direct comparisons between adsorbents and TF-SPME is challenging because the two methods necessitate different experimental conditions. However, the qualitative VOC profiles obtained by TF-SPME and adsorbents differ. Porapak® Q retained higher relative percentages of compounds with the shortest retention time—α-pinene, camphene, and β-pinene—while TF-SPME favored the compounds with the longest retention times—camphor, endo-borneol, and bornyl acetate. A similar qualitative trend can be observed within Porapak® Q in Fig. 1 . Two major factors that impact retention time are boiling point and polarity, properties that are related to, but not interchangeable with, the volatility of a compound. Though this suggests a correlation between compound volatility and the effectiveness of different VOC collection methods, we cannot draw a direct line between volatility and adsorbent performance. Additionally, the differences between these VOC profiles suggest that while TF-SPME may offer more comprehensive detection, adsorbents provide complementary data. The difference between collection methods also highlights the necessity of considering experimental bias when interpreting VOC data and making cross-study comparisons. In untargeted VOC analysis, there is no unbiased point of comparison, so it is impossible to tell how adsorbent choice might bias results and therefore challenging to unambiguously describe the VOC profile of any organism. Figure 3 helps visualize this bias. We cannot definitively say which method provides the most accurate representation of the volatile composition, but it is clear that they do not all paint the same picture. When choosing an adsorbent for targeted analysis, it is advisable to test multiple options to determine which one yields the best results. If none provide acceptable recovery, consider creating a calibration curve by collecting different concentrations of a standard mixture. For untargeted analysis, try multiple collection methods when possible, keeping in mind that bias is inevitable. This study provides valuable insights into the biases and limitations of various adsorbents in VOC collection. By understanding these biases, researchers can make more informed decisions when selecting adsorbents, leading to more accurate and reliable data in studies of chemical ecology and VOC-mediated biological interactions. The findings emphasize the need for continued evaluation and optimization of VOC collection methods to enhance our understanding of the complex roles VOCs play in ecological and evolutionary processes. Final Recommendations: Test different adsorbents for sensitivity and variability. Where possible, reduce flow rate and/or collection time to minimize break-through. Try different flow rates/collection times to determine the optimal parameters. Wash and dry sorbent tubes to remove contaminants. (Super Q is simply a solvent– and acid-washed Porapak® Q). If using dichloromethane (methylene chloride), transition to a safer and more environmentally friendly solvent with a similar relative polarity, such as methyl acetate. An internal standard helps account for instrument variability, but it may be trapped in certain adsorbents and not completely recovered. If necessary, add the internal standard to the final volume rather than the desorption solvent. Declarations Competing Interests. The authors declare they have no financial or non-financial conflict of interest. Funding. Funding for this project was provided by VOC Health. Author Contribution LS and CD conceived and designed the study. LS performed the experiments, collected, and analyzed the data. LS and CD wrote the manuscript. Acknowledgement This work was funded by VOC Health. Kenneth Tyler Wilcox and Leonardo Salgado provided support with statistical analysis. References Ali MF, Morgan ED (1990) Chemical communication in insect communities: a guide to insect pheromones with special emphasis on social insects. Biol Rev 65(3):227–247 https://doi.org/10.1111/j.1469-185X.1990.tb01425.x Alborn HT, Bruton RG, Beck JJ (2021) Sampling of Volatiles in Closed Systems: A Controlled Comparison of Three Solventless Volatile Collection Methods. J Chem Ecol 47:930–940. https://doi.org/10.1007/s10886-021-01306-6 Babushok VI, Linstrom PJ, Zenkevich IG (2011) Retention Indices for Frequently Reported Compounds of Plant Essential Oils. J Phys Chem Ref Data 40(4):043101. https://doi.org/10.1063/1.3653552 Bouwmeester H, Schuurink RC, Bleeker PM, Schiestl F (2019) The role of volatiles in plant communication Plant J 100:892–907. https://doi.org/10.1111/tpj.14496 Brown J, Shirey B (2001) A Tool for Selecting an Adsorbent for Thermal Desorption Applications. https://www.sigmaaldrich.com/deepweb/assets/sigmaaldrich/marketing/global/documents/103/692/t402025.pdf . Accessed 15 Jul 2024 Bruheim I, Liu X, Pawliszyn J (2003) Thin-film microextraction. 75(4):1002–1010. https://doi.org/10.1021/ac026162q Byrne KJ, Gore WE, Pearce GT et al (1975) Porapak-Q collection of airborne organic compounds serving as models for insect pheromones. J Chem Ecol 1:1–7. https://doi.org/10.1007/BF00987716 Brunke EJ, Hammerschmidt FJ, Schmaus G (1993) Flower Scent of Some Traditional Medicinal Plants. In: Tcranishi R, Buttery RG, Sugisawa H (eds) Bioactive Volatile Components from Plants. American Chemical Society, Washington DC, pp 282–296 Brunke EJ, Hammerschmidt FJ, Schmaus G (1994) Headspace analysis of hyacinth flowers. Flavour Frag J 9:59–69. https://doi.org/10.1002/ffj.2730090205 Calogirou A, Larsen BR, Brussol C, Duane M, Kotzias D (1996) Decomposition of Terpenes by Ozone during Sampling on Tenax. Anal Chem 68:1499–1506. https://doi.org/10.1021/ac950803i Pinto DM, Tiiva P, Miettinen P, Joutsensaari J, Kokkola H, Nerg AM, Laaksonen A, Holopainen JK (2007) The effects of increasing atmospheric ozone on biogenic monoterpene profiles and the formation of secondary aerosols. J Atmos Env 41:4877–4887. https://doi.org/10.1016/j.atmosenv.2007.02.006 Dudareva N, Negre F, Nagegowda DA, Orlova I (2006) Plant volatiles: recent advances and future perspectives. Crit Rev Plant Sci 25:417–440. https://doi.org/10.1080/07352680600899973 Dugheri S, Mucci N, Cappelli G, Trevisani L, Bonari A, Bucaletti E, Squillaci D, Arcangeli G (2022) Advanced Solid-Phase Microextraction Techniques and Related Automation: A Review of Commercially Available Technologies. J Anal Methods Chem 2022:8690569. https://doi.org/10.1155/2022/8690569 Eckert S, Eilers EJ, Jakobs R et al (2023) Inter-laboratory comparison of plant volatile analyses in the light of intra-specific chemodiversity. Metabolomics 19:62. https://doi.org/10.1007/s11306-023-02026-6 Emmons RV, Tajali R, Gionfriddo E (2019) Development, Optimization and Applications of Thin Film Solid Phase Microextraction (TF-SPME) Devices for Thermal Desorption: A Comprehensive Review. Separations 6:39. https://doi.org/10.3390/separations6030039 Fincheira P, Quiroz A, Tortella G, Diez MC, Rubilar O (2021) Current advances in plant-microbe communication via volatile organic compounds as an innovative strategy to improve plant growth. Microb Res 247:126726. https://doi.org/10.1016/j.micres.2021.126726 Heil M, Karban R (2010) Explaining evolution of plant communication by airborne signals Trends Ecol Evol 25:137–144. https://doi.org/10.1016/j.tree.2009.09.010 Holopainen JK, Gershenzon J (2010) Multiple stress factors and the emission of plant VOCs Trends Plant Sci 15:176–184. https://doi.org/10.1016/j.tplants.2010.01.006 Helmig D, Greenberg JP (1994) Automated in situ gas chromatographic-mass spectrometric analysis of ppt level volatile organic trace gases using multistage solid-adsorbent trapping. J Chromatogr A 677:123–132. https://doi.org/10.1016/0021-9673(94)80551-2 Jin J, Zhao M, Jing T, Zhang M, Lu M, Yu G, Wang J, Guo D, Pan Y, Hoffmann TD, Schwab W, Song C (2023) Volatile compound-mediated plant–plant interactions under stress with the tea plant as a model. Hortic Res 10:uhad143. https://doi.org/10.1093/hr/uhad143 Knudsen JT, Gershenzon J (2020) The chemical diversity of floral scent. In: Pichersky E Dudareva N (ed) Biology of Plant Volatiles, 2nd edn. CRC, Boca Raton Mann RS, Ali JG, Hermann SL, Tiwari S, Pelz-Stelinski KS, Alborn HT, Stelinski LL (2012) Induced release of a plant-defense volatile 'deceptively' attracts insect vectors to plants infected with a bacterial pathogen. PLoS Pathog 8:e1002610. https://doi.org/10.1371/journal.ppat.1002610 Morris BD, Smyth RR, Foster SP, Hoffmann MP, Roelofs WL, Franke S, Francke W (2005) Vittatalactone, a beta-lactone from the striped cucumber beetle, Acalymma vittatum. J Nat Prod 68:26–30. https://doi.org/10.1021/np049751v Nongonierma A, Voilley A, Cayot P, Le Quéré JL, Springett M (2006) Mechanisms of Extraction of Aroma Compounds from Foods, Using Adsorbents. Effect of Various Parameters. Food Rev Int 22:51–94. https://doi.org/10.1080/87559120500379951 Pawliszyn J (1997) Solid Phase Microextraction: Theory and Practice. Wiley, New York Paris TM, Johnston N, Strzyzewski I, Griesheimer JL, Reimer B, Malfa K, Allan SA, Martini X (2024) Tomato yellow leaf curl virus manipulates Bemisia tabaci, MEAM1 both directly and indirectly through changes in visual and volatile cues. PeerJ 12:e17665. https://doi.org/10.7717/peerj.17665 Pichersky E, Gershenzon J (2002) The formation and function of plant volatiles: perfumes for pollinator attraction and defense. Curr Opin Plant Bio 5:237–243 Renou M (2014) Pheromones and General Odor Perception in Insects. In: Mucignat-Caretta C (ed) Neurobiology of Chemical Communication. CRC Press/Taylor & Francis, Boca Raton Renou M, Anton S (2020) Insect olfactory communication in a complex and changing world. Curr Opin Insect Sci 42:1–7. https://doi.org/10.1016/j.cois.2020.04.004 Sharifi R, Je-Seung Jeon C-M, Ryu (2022) Belowground plant–microbe communications via volatile compounds. J Exp Bot 73:463–486. https://doi.org/10.1093/jxb/erab465 Tholl D, Hossain O, Weinhold A, Röse USR, Wei Q (2021) Trends and applications in plant volatile sampling and analysis. Plant J 106:314–325. https://doi.org/10.1111/tpj.15176 Turlings TCJ, Erb M (2018) Tritrophic interactions mediated by herbivore-induced plant volatiles: Mechanisms, ecological relevance, and application potential. Annu Rev Entomol 63:433–452. https://doi.org/10.1146/annurev-ento-020117-043507 Verma RS, Padalia RC, Chauhan A, Upadhyay RK, Ram Singh V (2020) Productivity and essential oil composition of rosemary (Rosmarinus officinalis L.) harvested at different growth stages under the subtropical region of north India. J Essent Oil Res 32:144–149. https://doi.org/10.1080/10412905.2019.1684391 Environmental Protection US Agency (1999) Compendium Method TO-17: Determination of Volatile Organic Compounds in Ambient Air Using Active Sampling Onto Sorbent Tubes. Center for Environmental Research Information, Cincinnati, OH Weisskopf L, Schulz S, Garbeva P (2021) Microbial volatile organic compounds in intra-kingdom and inter-kingdom interactions. Nat Rev Microbiol 19:391–404. https://doi.org/10.1038/s41579-020-00508-1 Xie Y, Lyu S, Zhang Y, Cai C (2022) Adsorption and Degradation of Volatile Organic Compounds by Metal-Organic Frameworks (MOFs): A Review. Materials 15:7727. https://doi.org/10.3390/ma15217727 Shaoqun Zhou, Jander G (2022) Molecular ecology of plant volatiles in interactions with insect herbivores. J Exp Bot 73:449–462. https://doi.org/10.1093/jxb/erab413 Additional Declarations No competing interests reported. Supplementary Files SupplementaryInformationUnderstandingyourbiasesincollectingorganismalVOCs.docx Cite Share Download PDF Status: Published Journal Publication published 14 Mar, 2025 Read the published version in Journal of Chemical Ecology → Version 1 posted Editorial decision: Revision requested 07 Feb, 2025 Reviews received at journal 06 Dec, 2024 Reviewers agreed at journal 02 Dec, 2024 Reviewers agreed at journal 25 Nov, 2024 Reviewers invited by journal 22 Nov, 2024 Editor assigned by journal 22 Nov, 2024 Submission checks completed at journal 19 Nov, 2024 First submitted to journal 15 Nov, 2024 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. 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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-5462922","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":384657093,"identity":"c12a4586-f50e-4cc1-b882-9ecf77480362","order_by":0,"name":"Lucas Seybert","email":"","orcid":"","institution":"Cornell University","correspondingAuthor":false,"prefix":"","firstName":"Lucas","middleName":"","lastName":"Seybert","suffix":""},{"id":384657095,"identity":"338baa3f-2ce8-4d3f-8822-c4c332fb40b6","order_by":1,"name":"Christophe Duplais","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA30lEQVRIiWNgGAWjYHACAwaGAgYeBvYGME+GD0hIENZiANTCcwDM42EjVgtQWQKRWvgbmLdJFxjYycjPfPzwceUOG6AW5oO3efBokTjAViY9wyCZx+B2mrHh2TNpQC1sydb4tAB9YSbNY8DMYyCdwybZ2HYYqAUkQlhLPY/8zDMgLf+BWvi/EaPlMA/DDR6QlgMgW9jwapE4zFZsPcPgOI/BGaBfGtuSediY2Ywt5+DRwt/evPF2QUW1vXz74YcPG9vs5PjZmx/eeINHCwMzGKGJEATEqBkFo2AUjIKRDAA8DzZzuA5wjgAAAABJRU5ErkJggg==","orcid":"","institution":"Cornell University","correspondingAuthor":true,"prefix":"","firstName":"Christophe","middleName":"","lastName":"Duplais","suffix":""}],"badges":[],"createdAt":"2024-11-15 21:23:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5462922/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5462922/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s10886-025-01592-4","type":"published","date":"2025-03-14T15:58:53+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":70389806,"identity":"c4398ee0-403c-4cfc-b517-4bdaa1e4b1ce","added_by":"auto","created_at":"2024-12-02 17:29:21","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":35916,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of recovery efficiencies for different adsorbents in the collection of VOCs. The boxplots represent the recovery percentages of each VOCs (N=5-12 replicates per adsorbent). Compounds are arranged in order of increasing retention time\u003c/p\u003e","description":"","filename":"Picture1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5462922/v1/7e06c2169d95891c0b1cc4a6.jpg"},{"id":70389672,"identity":"d4d5484a-96f7-4cfe-beb2-689a4037c556","added_by":"auto","created_at":"2024-12-02 17:29:04","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":16987,"visible":true,"origin":"","legend":"\u003cp\u003ePeak areas of VOCs collected from rosemary using different adsorbents (N=3 replicates per adsorbent). Areas were standardized by dividing by sample mass. Compounds are arranged in order of increasing retention time\u003c/p\u003e","description":"","filename":"Picture2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5462922/v1/5de54fc3d9f4a81d42c235fb.jpg"},{"id":70389652,"identity":"012b4fc0-4a06-41f2-b205-12ea33b71e96","added_by":"auto","created_at":"2024-12-02 17:29:02","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":27860,"visible":true,"origin":"","legend":"\u003cp\u003eRelative area percentage of eleven VOCs collected from rosemary. Compounds are arranged in order of increasing retention time\u003c/p\u003e","description":"","filename":"Picture3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5462922/v1/224b1b40ef2a9a03029bf11b.jpg"},{"id":78689140,"identity":"37c815df-8248-4d08-a8eb-f349a342a111","added_by":"auto","created_at":"2025-03-17 16:11:51","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":469293,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5462922/v1/ec9244fc-1355-4481-9925-9dfe2422933a.pdf"},{"id":70389653,"identity":"6b4ab19e-73a0-4466-baca-5a5fd1602e10","added_by":"auto","created_at":"2024-12-02 17:29:03","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":332686,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryInformationUnderstandingyourbiasesincollectingorganismalVOCs.docx","url":"https://assets-eu.researchsquare.com/files/rs-5462922/v1/42bc8193dd27cbf3df45dc36.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Understanding your biases in collecting organismal VOCs","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eVolatile organic compounds (VOCs) emitted by organisms play a fundamental role in biological interactions. These chemical molecules, released by an emitter, can be detected by a receiver, thus facilitating communication both within and between species (Knudsen and Gershenzon \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). VOCs are involved in processes such as plant-plant communication (Bouwmeester et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), plant-insect interactions (Zhou and Jander 2022), insect communications (Ali and Morgan \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1990\u003c/span\u003e: Renou \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Renou and Anton \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), tri-trophic interactions (Turlings and Erb \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), plant-microbe interactions (Fincheira et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Sharifi et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and microbe communications (Weisskopf et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), significantly influencing ecological dynamics and evolutionary trajectories (Pichersky and Gershenzon, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Dudareva et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Heil and Karban \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). VOC research also has practical applications: for instance, plants infected by viruses (Mann et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Paris et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) or experiencing abiotic stress conditions (Holopainen and Gershenzon \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Jin et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) exhibit distinct VOC signatures, which can serve as potential markers for assessing plant health. For all these reasons, accurate collection and analysis of VOCs are paramount for advancing our understanding of these complex biological interactions and environmental adaptations.\u003c/p\u003e \u003cp\u003eVOC collection methods can be divided into two broad categories: static and dynamic (Tholl et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Static collection, which often involves the use of solid-phase microextraction (SPME), allows the passive adsorption of VOCs onto a solid adsorbent matrix. Headspace SPME is based on the partition equilibrium of analytes between the sample matrix and the atmosphere followed by the thermal desorption (TD) of analytes (Bruheim et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Dugheri et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The first SPME device was an adsorbent fiber coated with polydimethylsiloxane (PDMS) (Pawliszyn \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e1997\u003c/span\u003e), with other adsorbent polymers subsequently becoming available. This method is valued for its simplicity and minimal equipment requirements but has limitations, such as the need for immediate analysis after VOC collection and difficulty in obtaining a high number of replicates due to the lack of GC autosampling for SPME fibers. The development of a thin film (TF) SPME, with a significantly higher surface area than traditional fiber SPME, combined with automated TD, has circumvented some of these drawbacks, and its increased sensitivity and relative ease of handling have expanded applications (Emmons et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). However, TF-SPME requires additional gas chromatography (GC) equipment like headspace autosamplers and thermal desorption units. All SPME technologies have the additional drawback that once VOCs are desorbed individual samples cannot be re-analyzed.\u003c/p\u003e \u003cp\u003eDynamic collection methods, such as the use of adsorbent tubes coupled to an air pump, actively draw air through an adsorbent material, resulting in the accumulation of VOCs (Helmig and Greenberg \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e1994\u003c/span\u003e). This method has historically played an important role in chemical ecology and the very first article in the Journal of Chemical Ecology reports the use of Porapak\u0026reg; Q to collect insect pheromones (Byrne et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e1975\u003c/span\u003e). The major advantage of dynamic collection is the ability to use solvent extraction, which facilitates the purification of large quantities of VOCs, allows for absolute quantification using external or internal standards, and produces samples that may be re-analyzed multiple times if necessary. Additionally, novel VOCs can be structurally characterized by trapping and purifying large quantities, such as in the case of the striped cucumber beetle (\u003cem\u003eAcalymma vittatum\u003c/em\u003e) aggregation pheromone vittatalactone (Morris et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2005\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe choice of adsorbent material significantly impacts the efficiency and reliability of VOC collection. Different adsorbents vary in their affinity for specific compounds, recovery rates, and susceptibility to saturation and contamination (Nongonierma et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Popular sorbents include carbon-based matrices (activated charcoal, graphite, molecular sieves) and synthetic polymers, which are available either as raw materials or pre-packed in sealed tubes. Navigating the chemical compositions of adsorbents can be challenging for non-chemists, especially when sold under brand names like the Orbo\u0026reg;, Hayesep\u0026reg; and Porapak\u0026reg; series. The specific polarity, surface area, and pore size of these adsorbents can affect their efficiency in collecting VOCs (Xie et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), introducing additional biases. Other confounding factors may arise from compounds reacting with the adsorbent matrix, such as when ocimene reacts with activated charcoal to give the oxidation products octatetraene and octatrienol (Brunke et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e1993\u003c/span\u003e). Finally, another bias related to environmental conditions is the degradation of certain terpenes in the presence of trace amounts of ozone (Calogirou et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e1996\u003c/span\u003e; Pinto et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Understanding these biases is crucial for selecting the appropriate collection method and adsorbent for specific ecological studies. Different adsorbents can lead to different chemical profile results, as described for floral VOCs using activated charcoal and Tenax\u0026reg; (Brunke et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e1994\u003c/span\u003e), but an updated comparison of several commercially available adsorbents has not been conducted recently. This paper investigates how VOC collection methods, in particular adsorbent choice, affects variability and recovery of VOCs, and aims to provide a comprehensive assessment of biases in VOC collection for studies in chemical ecology. We used both a synthetic mixture of VOCs and VOCs collected from rosemary (\u003cem\u003eRosemarinus officinalis\u003c/em\u003e) plants to compare the performance of different absorbents, and the variation of chemical diversity compared to TF-SPME. VOC analysis, regardless of the method used, reflects only part of the qualitative and quantitative reality of VOCs emitted by organisms; this study serves as not only a reminder of methodological shortcomings, but as a framework for understanding how VOC collection methods bias our understanding of the systems we study.\u003c/p\u003e"},{"header":"METHODS AND MATERIALS","content":"\u003cp\u003e \u003cem\u003eChemicals, Sorbent Tubes, and TF-SPME.\u003c/em\u003e HPLC-grade methyl acetate was purchased from MilliporeSigma (Saint Louis, MO). Linalool oxides, n-hexanal, and 2-phenylethyl alcohol were purchased from TCI (Tokyo, Japan). (\u003cem\u003eR\u003c/em\u003e)-(+)-limonene, cis-3-hexenyl acetate, (-)-linalool, and n-pentadecane were purchased from MilliporeSigma (Saint Louis, MO). Methyl salicylate was purchased from Chem-Impex (Wood Dale, IL). β-caryophyllene was purchased from ICN Biomedical (Costa Mesa, CA).\u003c/p\u003e \u003cp\u003ePre-packed sorbent tubes were obtained from the following vendors: Porapak\u0026reg; P (Divinylbenzene (DVB)/Styrene, ZST-125), Porapak\u0026reg; R (DVB/Vinyl methylpyrrolidone, ZST-135), and Porapak\u0026reg; N (DVB-ethylvinylbenzene-ethyleneglycol dimethacrylate, ZST-120) tubes were purchased from Zefon International; Orbo\u0026reg; 32 (activated charcoal), Orbo\u0026reg; 47 (Supelpak\u0026reg; 70\u0026thinsp;=\u0026thinsp;Amberlite XAD-7\u0026reg;, aliphatic acrylic polymer), Orbo\u0026reg; 90 (Carboxen-564, carbon molecular sieves), and Orbo\u0026reg; 101 (carbotrap, graphitized carbon black) tubes were purchased from MilliporeSigma; Tenax\u0026reg; (SKC 226-35-03) tubes were purchased from SKC Inc. Porapak\u0026reg; Q (DVB-ethylvinylbenzene) were purchased from MilliporeSigma (Orbo\u0026reg; 1103), Zefon International (ZST-130), and SKC Inc. (SKC 226\u0026thinsp;\u0026minus;\u0026thinsp;115). (Note that Super Q is Porapak\u0026reg; Q extensively washed with organic solvents and acids to remove residual monomers and impurities.) The adsorbents are conditioned by eluting 2 mL of methyl acetate to remove impurities and then dried at 50\u0026deg;C (120\u0026deg;F) overnight except for Tenax\u0026reg; and Orbo\u0026reg; 32 which did not have significative contmination. Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e summarizes the characteristics of absorbents (not only those used in this study) in terms of chemical composition, polarity, pore diameter, and surface area, and provides reference codes from different vendors.\u003c/p\u003e \u003cp\u003ePolydimethylsiloxane (PDMS)-DVB thin-film solid phase microextraction (TF-SPME) devices were purchased from Gerstel (Gerstel GmbH, M\u0026uuml;llheim an der Ruhr, Germany) and conditioned at 250\u0026deg;C for one hour prior to use.\u003c/p\u003e \u003cp\u003e \u003cem\u003ePreparation of the VOC Standard and Desorption Solvent.\u003c/em\u003e A 1 mg/mL solution of n-hexanol, n-hexanal, cis-3-hexenyl acetate, (R)-(+)-limonene, linalool oxide (1:1 mix of isomers), (-)-linalool, 2-phenylethyl alcohol, methyl salicylate, and β-caryophyllene was made using methyl acetate as a solvent. This mixture was used to prepare the VOC standard curve as well as the 250 \u0026micro;g/mL mixture used in the VOC standard trials. 1, 10, 50, and 100 \u0026micro;g/mL calibration standards, all containing 52.6 \u0026micro;g/mL n-pentadecane as an internal standard, were prepared from the VOC standard mixture.\u003c/p\u003e \u003cp\u003e \u003cem\u003eVOC Standard Trials.\u003c/em\u003e Holes were drilled in the lids of 480 mL glass jars (Qorpak\u0026reg;) and \u0026frac14; inch polypropylene compression fittings (JACO Manufacturing, Berea, OH) were inserted. To each jar was attached a portable air pump (Pocket Pump Touch, SKC) and a pre-packed sorbent tube. 40 \u0026micro;L of 250 \u0026micro;g/mL VOC standard mixture was spotted onto filter paper, which was placed in the bottom of each jar. The lids were closed tightly, and the mixture was allowed to evaporate for 20 minutes. Then air was pumped through the jars at 500 mL/min for 3h before the sorbent tubes were removed and promptly desorbed by loading desorption solvent (methyl acetate containing 52.6 \u0026micro;g/mL n-pentadecane as an internal standard) onto the tube and allowing it to drip through until the solvent filled a 250 \u0026micro;L vial insert. In addition, 40 \u0026micro;L of 250 \u0026micro;g/mL VOC standard was diluted to the same total volume using desorption solvent and the resulting samples (N\u0026thinsp;=\u0026thinsp;3) were run in parallel to the adsorbent samples in order to determine the recovery yield.\u003c/p\u003e \u003cp\u003e \u003cem\u003eLive Plant Trials.\u003c/em\u003e Rosemary (\u003cem\u003eSalvia rosmarinus\u003c/em\u003e) plants were purchased from a local garden center. Branches weighing 0.9\u0026ndash;1.5 g were removed from the plants and cut into three segments. These segments were enclosed in jars fitted with pumps and sorbent tubes, as described above, and allowed to equilibrate for 20 minutes before air was pumped through the system at 500 mL/min for 3 hours. The sorbent tubes were desorbed with HPLC-grade methyl acetate as described previously, and the resulting samples were spiked with 5 \u0026micro;L of a standard solution of 1.32 mg/mL n-pentadecane in methyl acetate. In parallel, cut rosemary segments were sealed in jars in which a TF-SPME device had been suspended, and the devices were allowed to equilibrate for 3 hours and 20 minutes before they were removed and analyzed via GC-MS.\u003c/p\u003e \u003cp\u003e \u003cem\u003eAnalysis by GC-MS.\u003c/em\u003e All samples were analyzed using an Agilent 7890B GC coupled with a 5977C MS (Agilent Technologies, Santa Clara, CA). The instrument was fitted with a CIS 4 inlet connected to either a septumless sampling head for liquid injection or TD3.5\u0026thinsp;+\u0026thinsp;thermal desorption unit (Gerstel GmbH, M\u0026uuml;llheim an der Ruhr, Germany). Liquid samples were analyzed using the following instrument parameters: 1 \u0026micro;L of liquid sample was injected at a 10:1 split (for the VOC standard trials) or in splitless mode (for the rosemary trials) onto a 60 m x 0.25 mm x 0.25 \u0026micro;m HP-5MS column (Agilent Technologies) with a notched glass inlet liner (Gerstel) at a constant He flow of 1.2 mL/min. The inlet was held at 40\u0026deg;C for 0.5 min and then ramped at 12\u0026deg;C/s to a final temperature of 280\u0026deg;C, where it was held for the remainder of the run. The GC oven program began at 45\u0026deg;C, then was ramped according to the following protocol: 10\u0026deg;C/min to 100\u0026deg;C; 3\u0026deg;C/min to 130\u0026deg;C; 10\u0026deg;C/min to 180\u0026deg;C; and finally 30\u0026deg;C/min to 280\u0026deg;C, where it was held for 5 minutes. The MS was operated in scan mode with a m/z range of 29\u0026ndash;550 and a solvent delay of 5.5 minutes.\u003c/p\u003e \u003cp\u003eTF-SPME samples were desorbed at 250\u0026deg;C for 5 minutes while the inlet was held at -35\u0026deg;C, and then the inlet was heated at 12\u0026deg;C/s to a final temperature of 270\u0026deg;C. A deactivated glass wool inlet liner was used. All other GC/MS parameters were identical to the method used for the plant adsorbent trials, except that the solvent delay was reduced to 1 minute.\u003c/p\u003e \u003cp\u003e \u003cem\u003eData Analysis.\u003c/em\u003e Agilent MassHunter Quantitative Analysis software was used to analyze data from the VOC standard trials. Recovery of all VOCs was quantified based on a calibration curve and recovery of the internal standard. Significant suppression (\u0026ge;\u0026thinsp;50%) of the internal standard was noted in Orbo\u0026reg; 32, 90, and 101 samples, so internal standard recovery was disregarded in the analysis of those trials. The resulting concentration of each VOC was expressed as a percent recovery compared to the amount obtained when spiking VOC mixture directly into the vial.\u003c/p\u003e \u003cp\u003eOpen-source software MZmine 4.0.8 was used to identify the primary volatile components in the rosemary adsorbent and TF-SPME samples. The MZmine processing wizard was configured for low resolution GC-EI data in the processing wizard batch configuration and the following custom parameters were entered: a noise threshold and minimum feature height of 150, m/z tolerance of 0.1, and a minimum of three signals in the deconvoluted spectrum. Samples were also analyzed in the Agilent Unknowns Analysis software using the 2020 NIST mass spectral library to tentatively identify features. The feature list generated by MZmine was manually compared to the output from Unknowns Analysis in order to annotate features. In the adsorbent samples, all compounds appearing in two or more samples from a single treatment were annotated, and a total of nine biogenic VOCs were identified as compounds of interest. Due to the large number (95) of features detected in TF-SPME samples compared to the low number (\u0026lt;\u0026thinsp;20) of features detected by all sorbent tubes, annotation was limited to the twenty features with the highest average recovery. Features that were identified as probable contaminants (e.g. breakdown products of polymers) were disregarded. The annotation was compared to documented retention indices of terpenes (Babushok et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) and previously characterized volatile components of rosemary (Verma et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) to corroborate the identities of each feature.\u003c/p\u003e \u003cp\u003eThe list of ten VOCs with the highest average recovery in the TF-SPME trials was consolidated with the list of nine VOCs identified in the adsorbent samples, and a total of eleven VOCs were identified as compounds of interest for the purposes of comparing adsorbents and TF-SPME. MassHunter Quantitative Analysis was used to calculate the area of the base peak of each compound, which was used as a proxy for concentration in the ensuing statistical analysis. As TF-SPME and adsorbent samples were run using different GC methods, area counts could not be compared directly; instead, the area of each compound was expressed as a percentage of the summed area of all peaks.\u003c/p\u003e \u003cp\u003e \u003cem\u003eStatistical Analysis.\u003c/em\u003e Statistical analyses were performed in R (v. 4.3.3) using RStudio (2024.09.1) as an environment. Analysis of variance was performed individually for each compound, with adsorbent as the treatment and adjusted area (or calculated concentration) as the dependent variable. Logarithmic scaling of the data or a Welch\u0026rsquo;s ANOVA was performed in cases where the data set violated the assumptions of a traditional ANOVA. Tukey\u0026rsquo;s HSD (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) was used to identify which treatments differed significantly from one another. Additional details of the statistical analyses may be found in the Supplementary Information.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003eAs expected, different adsorbents recovered significantly different quantities of VOCs from an artificial VOC blend. (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Porapak\u0026reg; P had the highest average recovery yield for all compounds except hexanal and exhibited relative standard errors (RSE) below 5% for most compounds. Orbo\u0026reg; 101 and Orbo\u0026reg; 47 also performed well, with \u0026gt;\u0026thinsp;50% average recovery yield for most compounds. Although there is no statistically significant difference between the recovery yields from Orbo\u0026reg; 101 and Orbo\u0026reg; 47, Orbo\u0026reg; 47 had lower relative standard error: the RSE for all compounds except methyl salicylate was \u0026lt;\u0026thinsp;5% in Orbo\u0026reg; 47 but ranged from 7.7\u0026ndash;12.4% with Orbo\u0026reg; 101. Porapak\u0026reg; N and Porapak\u0026reg; R had similar average recovery yields to Orbo\u0026reg; 47, but high variability, with RSEs exceeding 20% for many compounds. Orbo\u0026reg; 32 performed comparably to Orbo\u0026reg; 47 for most compounds, but had lower average recovery yields of \u003cem\u003eR\u003c/em\u003e-(+)-limonene and methyl salicylate. Tenax\u0026reg; had excellent reproducibility, with RSEs as low as 2%, but recovery was strongly dependent on VOC. 3-Hexenyl acetate and methyl salicylate were recovered as effectively by Tenax\u0026reg; as by Porapak\u0026reg; P, while hexanal and linalool oxides had average recovery yield of 6.3% and 4.0%, respectively, comparable with the worst-performing adsorbents. Porapak\u0026reg; Q produced average recovery yields between 19% and 43%, and despite having double the number of replicates of any other adsorbent tested (N\u0026thinsp;=\u0026thinsp;12 versus N\u0026thinsp;=\u0026thinsp;5\u0026ndash;6), RSEs were still between 20% and 30% for most compounds. Only Orbo\u0026reg; 90 yielded lower average recovery than Porapak\u0026reg; Q, with no more than 25% of any compound recovered and RSEs between 20% and 25%. Table S3 details the mean recovery and standard error (SE) for every combination of compound and adsorbent, and also shows which differences between adsorbents are statistically significant (Tukey\u0026rsquo;s HSD, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). For every compound tested, Porapak\u0026reg; P either had the highest average recovery, or was not significantly different from the adsorbent with the highest average recovery. There was no clear correlation between the recovery yield and material properties such as pore size, polarity, or surface area.\u003c/p\u003e \u003cp\u003eSuppression of the internal standard, n-pentadecane, was observed in all three carbon-based adsorbents (Orbo\u0026reg; 32, 90, and 101): the internal standard recovery was approximately 50% in Orbo\u0026reg; 90 and 101, and just 1%-3% in Orbo\u0026reg; 32. These trends were consistent and repeatable across multiple calibrations, and no significant suppression was noted in other adsorbents.\u003c/p\u003e \u003cp\u003ePre-packed sorbent tubes containing Porapak\u0026reg; Q were purchased from three different vendors (SKC, Zefon, and MilliporeSigma) to determine whether the source of the product had an impact on performance. Though qualitative differences were observed between the three vendors (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003ea), with the tubes from SKC generally having the highest recovery and the tubes from MilliporeSigma generally having the lowest, the variability within vendors was high enough that there was no significant difference between the three. However, significant differences in recovery were found between two different lots of tubes purchased from MilliporeSigma (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eb). Recovery data from both lots of MilliporeSigma tubes were included in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAmong the nine adsorbents tested, four were selected for the plant VOCs experiment: Porapak\u003csup\u003e\u0026reg;\u003c/sup\u003e P, Porapak\u003csup\u003e\u0026reg;\u003c/sup\u003e Q, Tenax\u0026reg;, and Orbo\u003csup\u003e\u0026reg;\u003c/sup\u003e 32. Untargeted analysis was performed in two steps: chemical features were identified using MZmine, then those features were annotated using MassHunter Unknowns Analysis. The list of VOCs identified using Porapak\u003csup\u003e\u0026reg;\u003c/sup\u003e P and Porapak\u003csup\u003e\u0026reg;\u003c/sup\u003e Q was almost identical: α-pinene, β-pinene, camphene, camphor, bornyl acetate, 1,8-cineole, (\u003cem\u003eR\u003c/em\u003e)-(+)-limonene, and terpinolene were detected in both sets of samples, and β-myrcene was detected using Porapak\u003csup\u003e\u0026reg;\u003c/sup\u003e P. In contrast, only bornyl acetate, (\u003cem\u003eR\u003c/em\u003e)-(+)-limonene, β-myrcene, and terpinolene were detected in the Tenax\u003csup\u003e\u0026reg;\u003c/sup\u003e samples. Orbo\u003csup\u003e\u0026reg;\u003c/sup\u003e 32 was ultimately removed from the analysis because no compounds were detected in the original experiment (N\u0026thinsp;=\u0026thinsp;3) nor in the additional experiment (N\u0026thinsp;=\u0026thinsp;5) performed to confirm the result was not due to experimental error. Because all samples were collected and run under identical conditions, the base peak area count of a given compound may be treated as a proxy for concentration (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Despite the fact that untargeted analysis of Porapak\u003csup\u003e\u0026reg;\u003c/sup\u003e P and Porapak\u003csup\u003e\u0026reg;\u003c/sup\u003e Q yielded a very similar list of compounds, the targeted quantitative analysis showed that significantly more β-myrcene and (\u003cem\u003eR\u003c/em\u003e)-(+)-limonene were collected by Porapak\u003csup\u003e\u0026reg;\u003c/sup\u003e P than Porapak\u003csup\u003e\u0026reg;\u003c/sup\u003e Q. Although the concentrations of α-pinene, camphene, and β-pinene were higher in Porapak\u003csup\u003e\u0026reg;\u003c/sup\u003e Q compared to Porapak\u003csup\u003e\u0026reg;\u003c/sup\u003eP samples, this result was not statistically significant (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Tenax\u003csup\u003e\u0026reg;\u003c/sup\u003e performed significantly more poorly than either Porapak\u003csup\u003e\u0026reg;\u003c/sup\u003e P or Porapak\u003csup\u003e\u0026reg;\u003c/sup\u003e Q: α-pinene, camphor, and endo-borneol were present in extremely low quantities in just one sample out of three, and camphene, β-pinene, and 1,8-cineole could not be detected at all.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eUnder similar experimental conditions, TF-SPME detected more compounds (Table S4) in greater quantities: TF-SPME peak areas were orders of magnitude higher, with some peaks showing evidence of detector saturation (Fig. S2). A total of 95 compounds were detected using TF-SPME during untargeted analysis, compared to 14 peaks detected with Porapak\u0026reg; P and 8 peaks detected with Porapak\u0026reg; Q and Tenax\u0026reg;. Though TF-SPME was dramatically more sensitive than any of the adsorbents tested, the list of compounds with the highest area counts was very similar to the list of compounds detected in the adsorbent samples: α-pinene, camphene, camphor, bornyl acetate, 1,8-cineole, (\u003cem\u003eR\u003c/em\u003e)-(+)-limonene, terpinolene, and β-myrcene were found among the top ten peaks with the highest areas. Endo-borneol and γ-terpinene were also in the top ten peaks; β-pinene was present in the TF-SPME samples but was not in the top ten peaks. Targeted analysis of the adsorbent samples detected γ-terpinene in all three adsorbents, and endo-borneol in Porapak\u0026reg; P and Porapak\u0026reg; Q, even though these compounds were not identified during untargeted analysis. This merged list of 11 compounds detected in Porapak\u0026reg; P, Porapak\u0026reg; Q, Tenax\u0026reg;, and TF-SPME was used for further qualitative comparison.\u003c/p\u003e \u003cp\u003eTF-SPME requires different experimental and analytical conditions, which makes drawing direct comparisons between results obtained with TF-SPME and adsorbents challenging. Considering each peak area as a percentage of the total area of all 11 peaks helps account for the differences in sensitivity between TF-SPME and adsorbents and gives us a way to visualize the overall VOC profile. Our results highlight qualitative differences between the VOC profiles yielded by different methods (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Compared to Porapak\u003csup\u003e\u0026reg;\u003c/sup\u003e P and TF-SPME, Porapak\u003csup\u003e\u0026reg;\u003c/sup\u003e Q has higher relative percentages of α-pinene, camphene, and β-pinene, the three compounds with the shortest retention times. In contrast, TF-SPME has higher relative quantities of camphor, endo-borneol, and bornyl acetate, the compounds with the longest retention times. Porapak\u003csup\u003e\u0026reg;\u003c/sup\u003e P falls somewhere between the two. The absence of several compounds in the Tenax\u003csup\u003e\u0026reg;\u003c/sup\u003e samples biases the results in favor of the compounds that were retained, though as in the VOC standard trials, there is no obvious chemical or physical trend in which compounds are favored by Tenax\u003csup\u003e\u0026reg;\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eOur data demonstrate that different adsorbents retain volatiles differently. This conclusion is neither novel nor unexpected: adsorbents are widely used for air sampling and have been rigorously tested for that application (Brown and Shirey \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; EPA 1999). However, industrial contaminants tend to be chemically distinct from VOCs produced by organisms, making it challenging to apply these methods to the field of chemical ecology. A few previous studies (Brunke et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e1994\u003c/span\u003e; Alborn et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) have sought to compare different VOC collection methods; our research seeks to expand upon and update these findings with a broader range of commercially available adsorbents, and to compare those adsorbents to the relatively new technology of thin film-SPME.\u003c/p\u003e \u003cp\u003eComparing VOC analyses is challenging because of the profound effect methodology can have on the results. A recent study revealed a high degree of biological variability in the plant VOCs reported by different laboratories, even when those laboratories used the same plant material and experimental design (Eckert et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). By testing different adsorbents using an artificial mixture of VOCs under controlled laboratory conditions, we sought to explore the quantitative differences between those adsorbents with minimal confounding variables. The compounds in the VOC mix were carefully selected to represent a range of plant volatiles with different chemical properties and ecological relevance; a complete list of those compounds, including their structure and biological significance, is given in Table S2.\u003c/p\u003e \u003cp\u003eIn our VOC standard trials, it was evident that different adsorbents retain VOCs differently. Two metrics were used to determine adsorbent performance: total recovery yield and relative standard error. High recovery yield is critical for untargeted analysis to ensure all VOCs can be strongly retained by the adsorbents with minimal loss, whereas low standard error is important for the quantification of VOCs. Porapak\u0026reg; P's superior recovery yield suggests that it may be particularly effective for a broad range of VOCs, whereas the low relative standard error of Tenax\u0026reg; make it an excellent choice for studies requiring consistent results across replicates. In addition to excellent recovery, Porapak\u0026reg; P had below-average standard error, making it a versatile choice but not necessarily the best for every system. Different ecological systems, such as plant, animal, and microbe volatiles, contain diverse types of volatiles with different structures (aliphatic, aromatic, heteroaromatic) and functional groups (hydrocarbon, alcohol, aldehyde, carboxylic acids, esters, ethers, amides, amines, thiols, etc.) The artificial VOC blend used in this study was designed to represent the diversity of plant VOCs, but for a different ecological system, another adsorbent may prove superior to Porapak\u003csup\u003e\u0026reg;\u003c/sup\u003e P.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eImportantly, no clear correlation was identified between the recovery yield and any single material-specific characteristic such as pore size, polarity, or surface area. However, we found that different lots of Porapak\u0026reg; Q sourced from the same supplier have different VOC recovery and variability. This implies that VOC collection efficiency is complex and susceptible to factors as unpredictable as manufacturing inconsistencies, and it underscores the need for empirical testing of adsorbents. In addition, the suppression of the internal standard in Orbo\u0026reg; 101, 90, and 32 illustrates the potential for unexpected interactions between adsorbents, VOCs, and standards.\u003c/p\u003e \u003cp\u003eFor the second experiment, we chose four adsorbents based on their performance in the VOC standard experiment and their use in chemical ecology studies: Porapak\u0026reg; P, Porapak\u0026reg; Q, Tenax\u0026reg;, and Orbo\u0026reg; 32. The difference between Porapak\u0026reg; P and Q was less striking in the rosemary experiment than in the VOC standard trial, but still present for certain VOCs. In all instances where there was a significant difference between the two, Porapak\u0026reg; P had higher recovery than Porapak\u0026reg; Q. Tenax\u0026reg; showed a similar trend to the first experiment, performing as well as Porapak\u0026reg; P and Q for some VOCs but failing to detect about half, with no clear trends regarding which compounds were lost. Orbo\u0026reg; 32 was unable to detect any compounds, likely due to the retention of molecules within the matrix. Coconut charcoal has been shown to strongly adsorb a range of different VOCs (Brown and Shirey, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2001\u003c/span\u003e) This is supported by the fact that extremely low levels of the internal standard n-pentadecane were detected in the Orbo\u0026reg; 32 samples during the VOC standard trials, implying that the internal standard in the extraction solvent was retained in the sorbent tube.\u003c/p\u003e \u003cp\u003eWhen comparing the results of the rosemary trials with the VOC standard trials, it is important to keep a few salient differences between the two experiments in mind. Firstly, because the rosemary trials were limited to a specific system, there is a high degree of chemical similarity between the VOCs detected: all are mono\u0026ndash; and sesquiterpenes and their derivatives. This is in contrast to the VOC standard trials, in which a much higher degree of chemical diversity was present. Secondly, plant tissue emits VOCs slowly, which results in less opportunity for break-through, where continued airflow causes a weakly adsorbed compound to desorb and be lost to the environment. In the VOC standard experiment, evaporation occurs much more rapidly, potentially increasing the rate of breakthrough. This raises one important variable in VOC collection that this study does not explore: air volume. Collection time and flow rate both impact the total volume of air that passes through the adsorbent tube, which in turn impacts the rate of break-through (Brown and Shirey \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2001\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDynamic collection methods using adsorbents can provide detailed VOC profiles but may miss certain compounds which TF-SPME is capable of detecting. TF-SPME detected a higher number of volatiles and showed evidence of detector saturation, indicating that TF-SPME is a more sensitive method for VOC detection. Drawing direct comparisons between adsorbents and TF-SPME is challenging because the two methods necessitate different experimental conditions. However, the qualitative VOC profiles obtained by TF-SPME and adsorbents differ. Porapak\u0026reg; Q retained higher relative percentages of compounds with the shortest retention time\u0026mdash;α-pinene, camphene, and β-pinene\u0026mdash;while TF-SPME favored the compounds with the longest retention times\u0026mdash;camphor, endo-borneol, and bornyl acetate. A similar qualitative trend can be observed within Porapak\u0026reg; Q in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Two major factors that impact retention time are boiling point and polarity, properties that are related to, but not interchangeable with, the volatility of a compound. Though this suggests a correlation between compound volatility and the effectiveness of different VOC collection methods, we cannot draw a direct line between volatility and adsorbent performance. Additionally, the differences between these VOC profiles suggest that while TF-SPME may offer more comprehensive detection, adsorbents provide complementary data. The difference between collection methods also highlights the necessity of considering experimental bias when interpreting VOC data and making cross-study comparisons.\u003c/p\u003e \u003cp\u003eIn untargeted VOC analysis, there is no unbiased point of comparison, so it is impossible to tell how adsorbent choice might bias results and therefore challenging to unambiguously describe the VOC profile of any organism. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e helps visualize this bias. We cannot definitively say which method provides the most accurate representation of the volatile composition, but it is clear that they do not all paint the same picture. When choosing an adsorbent for targeted analysis, it is advisable to test multiple options to determine which one yields the best results. If none provide acceptable recovery, consider creating a calibration curve by collecting different concentrations of a standard mixture. For untargeted analysis, try multiple collection methods when possible, keeping in mind that bias is inevitable.\u003c/p\u003e \u003cp\u003eThis study provides valuable insights into the biases and limitations of various adsorbents in VOC collection. By understanding these biases, researchers can make more informed decisions when selecting adsorbents, leading to more accurate and reliable data in studies of chemical ecology and VOC-mediated biological interactions. The findings emphasize the need for continued evaluation and optimization of VOC collection methods to enhance our understanding of the complex roles VOCs play in ecological and evolutionary processes.\u003c/p\u003e\n\u003ch3\u003eFinal Recommendations:\u003c/h3\u003e\n\u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eTest different adsorbents for sensitivity and variability.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eWhere possible, reduce flow rate and/or collection time to minimize break-through. Try different flow rates/collection times to determine the optimal parameters.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eWash and dry sorbent tubes to remove contaminants. (Super Q is simply a solvent\u0026ndash; and acid-washed Porapak\u0026reg; Q).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eIf using dichloromethane (methylene chloride), transition to a safer and more environmentally friendly solvent with a similar relative polarity, such as methyl acetate.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eAn internal standard helps account for instrument variability, but it may be trapped in certain adsorbents and not completely recovered. If necessary, add the internal standard to the final volume rather than the desorption solvent.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eCompeting Interests.\u003c/h2\u003e \u003cp\u003eThe authors declare they have no financial or non-financial conflict of interest.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding.\u003c/h2\u003e \u003cp\u003eFunding for this project was provided by VOC Health.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eLS and CD conceived and designed the study. LS performed the experiments, collected, and analyzed the data. LS and CD wrote the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThis work was funded by VOC Health. 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J Exp Bot 73:449\u0026ndash;462. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/jxb/erab413\u003c/span\u003e\u003cspan address=\"10.1093/jxb/erab413\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-chemical-ecology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"joce","sideBox":"Learn more about [Journal of Chemical Ecology](https://www.springer.com/journal/10886)","snPcode":"10886","submissionUrl":"https://submission.nature.com/new-submission/10886/3","title":"Journal of Chemical Ecology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"VOC, SPME, adsorbents, headspace","lastPublishedDoi":"10.21203/rs.3.rs-5462922/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5462922/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eVolatile organic compounds (VOCs) play a fundamental role in organismal interactions, facilitating intra- and interspecific communication. Accurate collection and analysis of VOCs are essential for understanding these interactions, but the choice of collection method and adsorbent material can introduce biases. This study investigates the variability and recovery yield in VOC collection using various adsorbents and thin-film solid-phase microextraction (TF-SPME). We compared the performance of nine adsorbents and TF-SPME in capturing a standard VOC mixture and VOCs from rosemary plants. Results show significant differences in efficiency among adsorbents, with Porapak\u0026reg; P generally providing the best recovery for most compounds. TF-SPME exhibited higher sensitivity and detected a broader range of volatiles compared to adsorbents, though qualitative profiles varied. Our findings underscore the importance of empirical testing in adsorbent selection and highlight the inherent biases in VOC collection methods. These insights aim to guide and empower researchers in making informed decisions regarding experimental design and data interpretation to achieve more accurate and reliable VOC results in chemical ecology studies.\u003c/p\u003e","manuscriptTitle":"Understanding your biases in collecting organismal VOCs","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-12-02 17:13:59","doi":"10.21203/rs.3.rs-5462922/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-02-08T00:29:45+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-12-06T14:22:37+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"313171332360249524402646793209839709193","date":"2024-12-02T07:55:35+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"49288443875489312208820321519328397651","date":"2024-11-25T15:13:38+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-11-22T16:20:57+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-11-22T16:17:55+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-11-19T12:07:54+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Chemical Ecology","date":"2024-11-15T21:13:03+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-chemical-ecology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"joce","sideBox":"Learn more about [Journal of Chemical Ecology](https://www.springer.com/journal/10886)","snPcode":"10886","submissionUrl":"https://submission.nature.com/new-submission/10886/3","title":"Journal of Chemical Ecology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"d7e74685-806e-4404-a5db-11a5d62a712c","owner":[],"postedDate":"December 2nd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-03-17T16:06:23+00:00","versionOfRecord":{"articleIdentity":"rs-5462922","link":"https://doi.org/10.1007/s10886-025-01592-4","journal":{"identity":"journal-of-chemical-ecology","isVorOnly":false,"title":"Journal of Chemical Ecology"},"publishedOn":"2025-03-14 15:58:53","publishedOnDateReadable":"March 14th, 2025"},"versionCreatedAt":"2024-12-02 17:13:59","video":"","vorDoi":"10.1007/s10886-025-01592-4","vorDoiUrl":"https://doi.org/10.1007/s10886-025-01592-4","workflowStages":[]},"version":"v1","identity":"rs-5462922","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5462922","identity":"rs-5462922","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}