Chemical identification of adhesive remains on prehistoric stone tools is of great interest for archaeologists, as the residues contain interesting information on tool use and the exploitation of natural resources by hominins. Adhesives were used to form a wrapping around the stone tool to protect the hand from the sharp edges and improve grip, or to secure a handle out of organic material to the stone tool. This invention, of adding a handle to a stone tool, marks a fundamental change in prehistoric technology. Adhesives can be manufactured from readily available exudates, like pine resin, but could also be man-made, in the case of birch tar that is obtained by dry distillation of birch bark. The glueing properties of the adhesives could be enhanced with the addition of an additive (e.g. charcoal, ochre, beeswax). Given that adhesive manufacture is considered to indicate planning abilities and complex thought, its identification in archaeological assemblages is important for understanding the evolution of human cognition. However, given long-term burial, organic residues on stone tools are generally significantly degraded, which raises numerous chemical challenges and interpretative difficulties that need to be tackled through close collaboration between archaeologists and chemists. Without this interaction between two vastly different research fields, studies can suffer from an overinterpretation of analytical data or a lack of understanding of the archaeological context. This review discusses the main pitfalls encountered in the chemical analysis of prehistoric adhesives and offers analytical recommendations to avoid them. Applying the analytical practices as proposed here will increase the reliability and credibility of the analytical results and allow a strong chemical foundation for the archaeological interpretations. The main focus is on the use of gas chromatography-mass spectrometry for the chemical identification of prehistoric adhesives; however, other commonly used analytical techniques are also briefly discussed.

Standardized quality assurance and quality control (QA/QC) practices are essential for reproducible GC-MS metabolomics, yet systematic documentation of current laboratory practices has been lacking. Here, as part of the Metabolomic Quality Assurance and Quality Control Consortium (mQACC), we surveyed 85 laboratories from 27 countries to characterize QA/QC implementation and establish evidence-based recommendations. Respondents represented diverse applications, with 79% performing untargeted analysis, 60% conducting targeted analyses, and 44% conducting both. While single column chromatography is clearly the norm, 24% of the participants used multidimensional chromatography to improve the separation of complex mixtures. Electron ionization with autotuning dominated >95% of the respondents, but more than 30% of the laboratories at least occasionally also used chemical ionization. While most laboratories used low-resolution mass spectrometers, almost half of the laboratories also performed GC-MS analyses on high-resolution QTOF or Orbitrap instruments. A strong consensus emerged on critical QA/QC practices: >90% of laboratories use internal standards for quality control, perform regular leak checks, and maintain injector systems through routine component replacement, spanning column (exchange/cuts), liners, syringes, and septa. Routine monitoring (>50%) involves method blanks, peak shape assessments, and systematic evaluation of intensity drifts, carryovers, and contamination. Retention indices coupled with mass spectral library matching served as the primary annotation approach (60%). Overall, a consensus of best practices in QA/QC and reporting emerged, providing evidence-based recommendations for high-quality GC-MS metabolomics.

Gas chromatography-mass spectrometry (GC-MS) remains a key technique in metabolomics, yet most workflows rely on chemical derivatisation to enable the analysis of non-volatile metabolites. Although derivatisation broadens metabolite coverage, it increases sample preparation time and may introduce additional analytical variabilities. In contrast, solid-phase microextraction (SPME) enables rapid, solvent-free sampling of volatile and semi-volatile compounds, representing an attractive alternative for non-targeted studies. However, methodological developments integrating SPME with comprehensive two-dimensional gas chromatography (GC × GC-MS) remain limited. In this study, a simultaneous multi-SPME GC × GC-TOFMS workflow was developed for the non-targeted screening of faecal samples. Three identical fibres were used simultaneously to generate technical replicates from a single biological sample resulting strong performances in terms of relative standard deviation (10%). Dedicated fibre storage containers and optimised storage conditions were also developed to preserve analyte stability between sampling and injection. In parallel, commonly used GC × GC column configurations were evaluated. The optimised workflow was applied to stool reference materials generated within an interlaboratory metabolomics study coordinated by the National Institute of Standards and Technology (NIST), investigating the effects of diet (vegan vs. omnivore) and sample preservation (aqueous vs. lyophilised) to develop the more adequate stool reference material. SPME results were compared with derivatisation-based metabolomic and lipidomic workflows. Multivariate analysis revealed clear discrimination between diets and storage conditions, while data-fusion analysis highlighted the complementary nature of volatile, metabolomic, and lipidomic profiles. This work provides practical guidance for developing robust GC × GC-MS workflows for complex biological matrices, highlighting the need of multi-extraction approaches for comprehensive analytical coverage.

The increasing detection of micro- and nanoplastics in the environment has raised the need for reliable and standardized analytical methods. Among the available techniques, pyrolysis-gas chromatography-mass spectrometry (Py-GC–MS) is widely applied since it is a powerful technique for both microplastic identification and quantification using polymer-specific markers. However, the quantification accuracy is still challenged by the sample type, and matrix interferences. In this study, calibration approaches were evaluated for polystyrene (PS) using aqueous polymer suspensions and a commercially available microplastic (MP) calibration mixture. For PS, calibration curves did not exhibit any differences correlated with the particle size. However, the MP mixture consistently produced higher styrene trimer signals due to contributions from styrene-containing copolymers and suspected decreased pyrolysis efficiency, leading to a PS quantification error of 64 % to 78 %. The use of poly(4-fluorostyrene) as internal standard reduced these discrepancies, reducing the quantification error up to 31 % for higher masses (1.25 µg), but did not completely eliminate them. These findings highlight the need to carefully consider the calibration type and the use of an internal standard when establishing calibration protocols for micro- and nanoplastic quantification by Py-GC–MS, as inappropriate calibration approaches may lead to substantial quantification bias, particularly when analyzing small particles where heat transfer effects can impact the pyrolysis process.

Comprehensive two-dimensional gas chromatography (GC × GC) systems can be broadly classified according to three important components: the modulator (cryogenic or flow-based), the column set (normal or reversed orthogonality), and the detector (MS or non-MS). In this work a methodology for the comparison of GC × GC systems has been developed. This method is based on the use of a synthetic mixture of chemicals called the Century Mix, which provides a fingerprint of the relative retention coordinates of the mixture components on each system. These retention coordinates are then normalized through the use of temperature-programmed retention indexing (TPRI), and the comparison of these TPRI values from one system to another can establish the degree to which they are similar. A metric called the retention index correlation (RIC) is introduced to measure the goodness-of-fit between systems. In the first phase of the project, the variation of operating parameters (such as flow rate, temperature programming rate, and modulation period) were investigated to determine their impact on the reproducibility of the TPRI values on a single instrument. In the second phase of the project, Century Mix fingerprints were generated on 11 different GC × GC systems, and pairwise comparisons between thermal and flow-based systems with the same stationary phase combination yielded RIC plot r2 values above 0.999. Advanced data processing techniques (such as principal component analysis, and dendrogram and heatmap plots) were also used to characterize and classify the bidimensional retention maps as a collective group. The method successfully demonstrates the ability to distinguish between significant differences in systems, and will ultimately be helpful in establishing effective comparisons in larger-scale studies.

Exhaled breath analysis represents a promising non-invasive approach for disease monitoring through volatile organic compounds (VOCs) detection. However, the lack of standardized sampling methods do not enable direct clinical translation. This study compared three widely used offline breath sampling techniques (Tedlar® bags, BioVOC-2®, and ReCIVA®) using the established peppermint benchmarking protocol and comprehensive two-dimensional gas chromatography coupled to mass spectrometry (GC × GC-MS). Seven healthy participants completed the peppermint experiment, with breath samples collected at multiple time points following capsule ingestion. Washout curves for targeted terpenoid compounds were analyzed to assess analytical performance, reproducibility, and background contamination across devices. Clinical feasibility was evaluated through focus groups with clinicians, researchers, and study participants. Tedlar® bags demonstrated reliable performance with lowest overall pooled relative standard deviations, though sensitive to exogenous contamination. ReCIVA® showed higher overall variability, superior selectivity and reduced background interference compared to Tedlar® bags (p< 0.01). However, ReCIVA® showed higher complexity, cost, reduced comfort and potential for saliva contamination during extended sampling. BioVOC-2® offered operational simplicity but was limited by small sampling volume (129 ml) reducing its sensitivity and manual handling variability. No single device emerged as universally optimal. Tedlar® bags, when accompanied by rigorous standard operating procedures, remain most suitable for large-scale studies, BioVOC-2® for rapid targeted screening, and ReCIVA® for controlled research requiring high selectivity. Successful clinical implementation will require balancing analytical performance with practical considerations including patient comfort, cost-effectiveness, and workflow integration. These findings support ongoing standardization efforts within the breathomics community and extend peppermint database for exhaled breath sampling.

Adhesives were used by prehistoric humans for attaching a handle to a stone tool, to improve tool use. Remains of these adhesives preserve on stone tools until today. Chemical analysis of these residues is essential for an improved understanding of how humans exploited their natural environment, stone tool manufacturing and use. However, chemical analysis is not straightforward, the highly degraded residue and the precious artefacts impose limitation. In this study a novel (semi-) non-destructive identification technique for prehistoric hafting adhesives is reported; dynamic headspace sampling coupled to comprehensive two-dimensional GC-MS. The dynamic sampling results in a full characterization of the volatile profile of the adhesives. A major advantage is that the whole stone tool, with the adhering adhesive, can be analyzed. Moreover, good results are obtained using only slightly elevated temperatures, which avoids heat damage to the stone tools. Nonetheless, the established biomarkers for prehistoric adhesives are not extracted with this method. Therefore, a non-targeted analytical approach combined with multivariate analysis is utilized. In this approach, the chromatogram of an unknown sample is compared to a database of known samples. In this study a start of an adhesive database is made with 14 different adhesives divided over 4 adhesive classes. The identification capability of this technique is further evaluated using six experimental stone tools, with different adhesives adhered to them and subjected to UV-induced degradation. The large stone pieces could not fit in the automated sampling station, thus, a manual sampling set-up was build. It was found that the sampling strategy did not affect the volatiles extracted and that comparison with the database was possible. The tar samples were the least affected and could be easily identified while the resin samples were more degraded and identification was difficult. This technique is promising for non-destructive adhesive identification on prehistoric stone tools.

Volatile compounds (VCs) produced by most host-associated bacteria remain largely unexplored despite their potential roles in suppressing microbial competitors and facilitating host colonization. This study investigated the volatilome of Streptomyces scabiei 87-22, the model species for causative agents of common scab in root and tuber crops, under culture conditions that completely inhibited fungal growth, including the phytopathogens Alternaria solani and Gibberella zeae. Bicameral assays confirmed that these effects were partially due to VCs. Using gas chromatography coupled with time-of-flight mass spectrometry, 36 VCs were unambiguously identified as products of S. scabiei 87-22 metabolic activity. These included mainly ketones and aromatic compounds (both benzene derivatives and heterocycles), along with smaller contributions from other chemical families, including sulfur-containing compounds, nitriles, esters, terpenoids, an amide, and an aldehyde. A literature survey suggests that many of these VCs possess antibacterial, antifungal, anti-oomycete, nematocidal, and insecticidal effects, while the bioactivity of others remains speculative, having been identified only within complex volatile mixtures. Among those with known antifungal properties, dimethyl trisulfide, 2-heptanone, and creosol inhibited the growth of the fungal pathogens tested in this study. In addition, we reveal here that 3-penten-2-one is also a strong inhibitor of fungal growth. Remarkably, despite S. scabiei 87-22 being defined as a pathogen, some of its VCs were associated with plant growth promotion and defense stimulation. Overall, our work highlights the remarkable potential of S. scabiei 87-22 to produce VCs with diverse antagonistic activities and suggests that its ecological function in nature is likely more complex than the current view, exclusively centered on its pathogenicity. IMPORTANCE:This study reveals that Streptomyces scabiei, the bacterium causing common scab in root and tuber crops, produces a wide variety of volatile chemicals with surprising benefits. These natural compounds can inhibit the growth of other harmful microbes, including fungal plant pathogens. Some of these chemicals are already known to fight pests and diseases, while others, like 3-penten-2-one, are newly discovered as potential antifungals. Even more unexpectedly, some of the identified compounds may help plants grow or boost their defenses. Combined with previous work, our findings challenge the idea that S. scabiei is purely harmful and suggest it might, under certain conditions, stimulate plant defense and can act protectively in its environment.

BACKGROUND:Polycyclic aromatic heterocycles containing nitrogen, sulfur and oxygen (NSO-HET) are toxic and persistent contaminants commonly found in fuels. The simultaneous determination of NSO-HET is hindered by matrix complexity, isomer overlap, and coelution with aromatic compounds. Conventional approaches rely on laborious steps like fractionation to reduce the complexity of the matrix before chromatographic analysis. The resolution power of the comprehensive two-dimensional gas chromatography coupled with time-of-flight mass spectrometric (GC × GC-ToFMS) allows fewer sample preparation steps; however, quantification studies by this technique are limited. This study addressed the analytical challenge of simultaneously quantifying NSO-HET in complex fuels by GC × GC-ToFMS with minimal sample preparation. RESULTS:An offline preparative high-performance liquid chromatography (HPLC) method was applied to enrich the aromatic fraction and remove interferences, followed by GC × GC-ToFMS analysis of 55 NSO-HET across six fuel samples. Sample preparation was simplified, resulting in a reduction of solvent and sample manipulation. The method was validated and demonstrated adequate performance, with low limits of detection and quantification (0.34-70.34 ng mL-1), recoveries from 56.6 % to 103 % and RSD values below 20 %. The method was successfully applied to both unfractionated and fractionated fuels. Marine diesel and diesel S-500 showed the highest concentrations, with 2,5,7-trimethylbenzothiophene and dibenzofuran at 943 ± 85.7 ng mL-1 and 884 ± 31.7 ng mL-1, respectively. Principal component analysis revealed significant discrimination among the samples based on compound class and concentration. This is the first study on the simultaneous quantification of a wide range of NSO-HET compounds in fuels samples by GC × GC-ToFMS. SIGNIFICANCE:The offline HPLC-GC × GC-ToFMS validated method enabled the accurate and simultaneous quantification of 55 individual NSO-HETs, including benzothiophenes, dibenzothiophenes, naphthobenzothiophenes, carbazoles, indoles, and dibenzofurans, in different fuels. It provides a robust alternative to traditional approaches for both industrial and research applications.

Meat quality traits are economically important in pig production. Breeding strategies can help prevent meat defects such as boar taint, usually characterized by quantified indole, skatole and androstenone (ISA) in back fat. This exploratory study investigated the genetic potential of a novel boar taint phenotype, pooling volatile organic compounds (VOCs), which were recently identified as phenotypically discriminant. Fat samples were collected from 1272 Pietrain x Landrace crossbred boars. Phenotypes for boar taint on these samples were: lab sensory score (LSS; n = 1269), ISA quantification (n = 308), and VOC profiles (n = 127). Given the limited amount of data, a selection index-based approach was used to pool traits in trait groups, ISA and VOC, considering LSS as reference trait. (Co)variance components were estimated with a full multi-trait model, and index equations were adjusted to account for uncertainty in estimated parameters. Index coefficients were then applied to ISA and VOC phenotypes to generate two pooled phenotypes, ISA and VOC indices. Estimates from the 3-trait model (LSS, ISA index and VOC index) confirmed high expected correlations with LSS. Genetic parameter estimates showed higher significance demonstrating the interest of pooling multiple partially informative traits together. Moreover, using the VOC index would generate a higher expected correlated genetic response in LSS (192 %) than the ISA index (160 %) compared to the direct response when using only LSS. Despite limited data, this exploratory study showed the potential of this novel broad phenotype based on pooled VOCs to improve genetic selection for reduced boar taint risk, although further validation in larger populations is required.

The clinical diagnosis of dermatophytosis and identification of dermatophytes face challenges due to reliance on culture-based methods. Rapid, cost-effective detection techniques for volatile organic compounds (VOCs) have been developed for other microorganisms, but their application to dermatophytes is limited. This study explores using VOCs as diagnostic markers for dermatophytes. We compared VOC profiles across different dermatophyte taxa using solid-phase microextraction (SPME) and advanced analytical methods: gas chromatography-mass spectrometry (GC–MS) and comprehensive two-dimensional gas chromatography with time-of-flight mass spectrometry (GC×GC-TOFMS). We analyzed 47 dermatophyte strains from 15 taxa grown on sheep wool, including clinically significant species. Additionally, we examined phylogenetic relationships among the strains to correlate genetic relatedness with metabolite production. Our results showed that GC×GC-TOFMS offered superior resolution but similar differentiation of VOC profiles compared to GC–MS. VOC spectra allowed reliable distinction of taxonomic units at the species level and below, however, these distinctions showed only a slight correlation with phylogenetic data. We identified pan-dermatophyte and species- or strain-specific VOC profiles, indicating their potential for rapid, non-invasive detection of dermatophyte infections, including epidemic strains. These patterns could enable future taxa-specific identification. Our study highlights the potential of VOCs as tools for dermatophyte taxonomy and diagnosis.

The European Chemicals Agency regulations have recently begun encouraging greener chemistry across all sectors. Armament manufacturers are very interested in moving forward by replacing propellant stabilizers with natural products, which aligns with the green chemistry principle of designing safer chemicals. This article highlights variabilities in the volatiles emitted from propellant powders during aging (from STANAG 4582 [NATO Standardization Agreement]) containing three green stabilizers: alpha-ionone, alpha-tocopherol, and hydroxyl-terminated polybutadiene. Headspace solid-phase microextraction was applied in combination with comprehensive two-dimensional gas chromatography-time-of-flight mass spectrometry (GC x GC-TOFMS) to analyze the volatile organic compound (VOC) fraction coming from the aging of propellants with three different green stabilizer additives. Principal component analysis (PCA) was used to compare the evolution of VOC profiles over time. The VOC profile from samples less than 5 years aged did not cluster closely on the PCA score plot, indicating variation in their VOC profile based on the number and amount of compounds. Samples greater than 5 years aged demonstrated stable composition with a similar number and amount of compounds present in the VOC profile. The VOC profile demonstrated a shift from fresh to degraded samples, demonstrating that this workflow could be useful to monitor aging progression in the routine quality control of stabilizers.

Comprehensive 2D gas chromatography coupled with mass spectrometry (GC × GC-MS) is a powerful analytical technique. However, the complexity and volume of data generated pose significant challenges for data processing and interpretation, limiting a broader adoption. Chemometric approaches, particularly multiway models like Parallel Factor Analysis (PARAFAC), have proven effective in addressing these challenges by enabling the extraction of meaningful chemical information from multi-dimensional datasets. However, traditional PARAFAC is constrained by its assumption of data tri-linearity, which may not be valid in all cases, leading to potential inaccuracies. To overcome these limitations, we present GcDUO, an open-source software implemented in R, designed specifically for the processing and analysis of GC × GC-MS data. GcDUO integrates advanced chemometric methods, including both PARAFAC and PARAFAC2, for a more accurate and comprehensive analysis. PARAFAC is particularly useful for deconvoluting overlapping peaks and extracting pure chemical signals, while PARAFAC2 relaxes de tri-linearity constraint, allowing the alignment between samples. The software is structured into six modules-data import, region of interest (ROI) selection, deconvolution, peak annotation, data integration, and visualization-facilitating comprehensive and flexible data processing. GcDUO was validated against the gold-standard software for comprehensive GC, demonstrating a high correlation (R2 = 0.9) in peak area measurements, confirming its effectiveness and reliability. GcDUO provides a valuable, open-source platform for researchers in metabolomics and related fields, enabling more accessible and customizable GC × GC-MS data analysis.

Members of the phylum Actinomycetota, particularly Streptomyces species, are prolific producers of bioactive metabolites, and bioprospecting in unique environments may uncover novel species producing previously undescribed antifungal compounds. In previous work, Streptomyces strains isolated from cave moonmilk deposits completely inhibited the growth of Rasamsonia argillacea, an emerging fungal pathogen associated with chronic granulomatous disease (CGD) and cystic fibrosis (CF). Cross-streak and bipartite Petri dish assays revealed that R. argillacea inhibition occurred specifically when bacteria were cultivated on Mueller-Hinton agar (MHA) and was mediated by volatile compounds rather than diffusible metabolites. This antifungal effect was not strain specific, as it was reproduced by phylogenetically diverse bacteria grown on MHA, suggesting the involvement of ubiquitous volatile molecules. Similar inhibitory effects were observed on MHA against other fungi and yeasts relevant to CGD and CF, supporting the hypothesis that these volatiles are broadly toxic rather than species-specific. To identify candidate ubiquitous antifungal VCs, we conducted comparative volatilomics of two phylogenetically distant bacterial strains that consistently inhibited fungal growth. Among the 143 VCs detected, only dimethyl trisulfide (DMTS) and dimethyl disulfide (DMDS) showed consistent presence in both bacterial volatilomes, with significantly increased production under conditions that promoted fungal inhibition. Exposure assays with pure compounds confirmed that both DMDS and DMTS strongly inhibited R. argillacea growth, with DMTS exhibiting greater potency. Our findings position these ubiquitous sulfur compounds as valuable models for exploring novel agents against human pathogens. IMPORTANCE:Our findings suggest that antifungal activity against human pathogens may arise from common metabolic pathways shared across diverse microbes rather than from unique biosynthetic systems. Because dimethyl disulfide and dimethyl trisulfide can also be generated through microbial and dietary sulfur metabolism, it is plausible that the human microbiome may produce similar volatiles depending on diet composition, particularly following consumption of sulfur-rich foods. This study, therefore, underscores the critical influence of culture conditions on revealing bioactive volatile production and opens intriguing perspectives on the ecological and physiological roles of ubiquitous microbial metabolites in regulating fungal colonization and microbiome-host interactions.

The growing environmental and health concerns regarding micro- and nanoplastics (MNPs) have prompted the development of advanced analytical methods for accurate characterization and quantification. Pyrolysis-gas chromatography-mass spectrometry (Py-GC-MS) enables polymer identification by their thermal destruction into characteristic fragments. However, the small particle size and interferences originating from complex sample matrices complicate its analysis. Therefore, the integration of comprehensive two-dimensional GC (GC×GC) would improve separation efficiency and sensitivity and provide a detailed composition of environmental and biological samples. This review documents (i) the evolution of Py-GC-MS and (ii) the potential to resolve overlapping compounds, improving quantification accuracy, and detecting minor plastic compounds and degradation byproducts by comprehensive GC×GC-MS as a crucial approach to measure MNPs. Despite the documented advancements, key challenges persist. The lack of standardized protocols for sample preparation and calibration, impeding the comparability of studies, is of prime concern. The massive presence of (in)organic interferences even further accentuates the absence of internal standards in terms of quantification. Therefore, to improve analytical reliability, future research should focus on developing standardized methodologies, improving detection sensitivity for NPs, and incorporating complementary approaches. Additionally, coupling GC×GC with time-of-flight MS further strengthens its capability to provide higher analytical resolution power and better chemical description of pyrolyzates. This review highlights the crucial role of advanced Py and chromatography-based techniques in supporting the analytical description of the extent of plastic pollution and in supporting evidence-based policymaking and successful mitigation efforts to protect ecosystems and public health.

One-dimensional gas chromatography (1D-GC) stationary phases are generally classified according to their relative polarity into non-polar, semi-polar, and polar columns. In comprehensive two-dimensional gas chromatography (GC×GC), it is the polarity difference between the two tandem-assembled stationary phases that determines the selectivity of the column ensemble. This polarity difference is called orthogonality, and GC×GC column sets can be broadly categorized into four groups based on the direction of the serial coupling between the primary and secondary columns. A significant portion of the GC×GC column sets in use today are operated in forward-orthogonality mode, which means that the secondary column is more polar than the primary column. A growing number of reported GC×GC applications operate in reversed-orthogonality configurations, where the secondary column is less polar than the primary column. Very few examples exist of non-orthogonal column sets because of the fact that there is limited additional selectivity that the secondary column can offer over the separation that has already been achieved in the primary column when the two phases are either identical or close in polarity. The fourth group of GC×GC column sets, called hybrid orthogonality, involves the coupling of stationary phases with peculiar selectivity differences that manifest themselves in the bi-dimensional separation plots. In this work, we are presenting a normalized approach to GC×GC column set characterization that is based on the use of a reference mixture of standards called the Century Mix. The Century Mix contains 100 chemical probes of different functionalities that span a reasonable range of volatilities and polarities to capture the selectivity profile of any GC×GC column set. The Century Mix also contains important chemical probes that are used for 1D-GC column characterization (such as the Grob mix and the Rohrschneider/McReynolds compounds) to make some connections between the 1D-GC building block columns and the GC×GC column sets. We finally also outline some other important metrics of comparison that should be taken into consideration to assess the overall performance of a GC×GC system.

This study aims to characterize a complete volatile organic compound profile of pork neck fat for boar taint prediction. The objectives are to identify specific compounds related to boar taint and to develop a classification model. In addition to the well-known androstenone, skatole and indole, 10 other features were found to be discriminant according to untargeted volatolomic analyses were conducted on 129 samples using HS-SPME-GC×GC-TOFMS. To select the odor-positive samples among the 129 analyzed, the selection was made by combining human nose evaluations with the skatole and androstenone concentrations determined using UHPLC-MS/MS. A comparison of the data of the two populations was performed and a statistical model analysis was built on 70 samples out of the total of 129 samples fully positive or fully negative through these two orthogonal methods for tainted prediction. Then, the model was applied to the 59 remaining samples. Finally, 7 samples were classified as tainted.

IntroductionHuman metabolomics has made significant strides in understanding metabolic changes and their implications for human health, with promising applications in diagnostics and treatment, particularly regarding the gut microbiome. However, progress is hampered by issues with data comparability and reproducibility across studies, limiting the translation of these discoveries into practical applications.ObjectivesThis study aims to evaluate the fit-for-purpose of a suite of human stool samples as potential candidate reference materials (RMs) and assess the state of the field regarding harmonizing gut metabolomics measurements.MethodsAn interlaboratory study was conducted with 18 participating institutions. The study allowed for the use of preferred analytical techniques, including liquid chromatography-mass spectrometry (LC-MS), gas chromatography-mass spectrometry (GC-MS), and nuclear magnetic resonance (NMR).ResultsDifferent laboratories used various methods and analytical platforms to identify the metabolites present in human stool RM samples. The study found a 40% to 70% recurrence in the reported top 20 most abundant metabolites across the four materials. In the full annotation list, the percentage of metabolites reported multiple times after nomenclature standardization was 36% (LC-MS), 58% (GC-MS) and 76% (NMR). Out of 9,300 unique metabolites, only 37 were reported across all three measurement techniques.ConclusionThis collaborative exercise emphasized the broad chemical survey possible with multi-technique approaches. Community engagement is essential for the evaluation and characterization of common materials designed to facilitate comparability and ensure data quality underscoring the value of determining current practices, challenges, and progress of a field through interlaboratory studies.