Distinguish Esophageal Cancer Cells through VOCs Induced by Methionine Regulation.

Detection of exhaled volatile organic compounds (VOCs) is promising for noninvasive screening of esophageal cancer (EC). Cellular VOC analysis can be used to investigate potential biomarkers. Considering the crucial role of methionine (Met) during cancer development, exploring associated abnormal metabolic phenotypes becomes imperative. In this work, we employed headspace solid-phase microextraction-gas chromatography-mass spectrometry (HS-SPME-GC-MS) to investigate the volatile metabolic profiles of EC cells (KYSE150) and normal esophageal epithelial cells (HEECs) under a Met regulation strategy. Using untargeted approaches, we analyzed the metabolic VOCs of the two cell types and explored the differential VOCs between them. Subsequently, we utilized targeted approaches to analyze the differential VOCs in both cell types under gradient Met culture conditions. The results revealed that there were five/six differential VOCs between cells under Met-containing/Met-free culture conditions. And the difference in levels of two characteristic VOCs (1-butanol and ethyl 2-methylbutyrate) between the two cell types intensified with the increase of the Met concentration. Notably, this is the first report on VOC analysis of EC cells and the first to consider the effect of Met on volatile metabolic profiles. The present work indicates that EC cells can be distinguished through VOCs induced by Met regulation, which holds promise for providing novel insights into diagnostic strategies.

Longitudinal Exposomics in a Multiomic Wellness Cohort Reveals Distinctive and Dynamic Environmental Chemical Mixtures in Blood.

Chemical exposomes can now be comprehensively measured in human blood, but knowledge of their variability and longitudinal stability is required for robust application in cohort studies. Here, we applied high-resolution chemical exposomics to plasma of 46 adults, each sampled 6 times over 2 years in a multiomic cohort, resulting in 276 individual exposomes. In addition to quantitative analysis of 83 priority target analytes, we discovered and semiquantified substances that have rarely or never been reported in humans, including personal care products, pesticide transformation products, and polymer additives. Hierarchical cluster analysis for 519 confidently annotated substances revealed unique and distinctive coexposures, including clustered pesticides, poly(ethylene glycols), chlorinated phenols, or natural substances from tea and coffee; interactive heatmaps were publicly deposited to support open exploration of the complex (meta)data. Intraclass correlation coefficients (ICC) for all annotated substances demonstrated the relatively low stability of the exposome compared to that of proteome, microbiome, and endogenous small molecules. Implications are that the chemical exposome must be measured more frequently than other omics in longitudinal studies and four longitudinal exposure types are defined that can be considered in study design. In this small cohort, mixed-effect models nevertheless revealed significant associations between testosterone and perfluoroalkyl substances, demonstrating great potential for longitudinal exposomics in precision health research.

LC-ESI-HRMS - lipidomics of phospholipids : Characterization of extraction, chromatography and detection parameters.

Lipids are a diverse class of molecules involved in many biological functions including cell signaling or cell membrane assembly. Owing to this relevance, LC-MS/MS-based lipidomics emerged as a major field in modern analytical chemistry. Here, we thoroughly characterized the influence of MS and LC settings - of a Q Exactive HF operated in Full MS/data-dependent MS2 TOP N acquisition mode - in order to optimize the semi-quantification of polar lipids. Optimization of MS-source settings improved the signal intensity by factor 3 compared to default settings. Polar lipids were separated on an ACQUITY Premier CSH C18 reversed-phase column (100 × 2.1 mm, 1.7 µm, 130 Å) during an elution window of 28 min, leading to a sufficient number of both data points across the chromatographic peaks, as well as MS2 spectra. Analysis was carried out in positive and negative ionization mode enabling the detection of a broader spectrum of lipids and to support the structural characterization of lipids. Optimal sample preparation of biological samples was achieved by liquid-liquid extraction using MeOH/MTBE resulting in an excellent extraction recovery > 85% with an intra-day and inter-day variability < 15%. The optimized method was applied on the investigation of changes in the phospholipid pattern in plasma from human subjects supplemented with n3-PUFA (20:5 and 22:6). The strongest increase was observed for lipids bearing 20:5, while 22:4 bearing lipids were lowered. Specifically, LPC 20:5_0:0 and PC 16:0_20:5 were found to be strongest elevated, while PE 18:0_22:4 and PC 18:2_18:2 were decreased by n3-PUFA supplementation. These results were confirmed by targeted LC-MS/MS using commercially available phospholipids as standards.

Non-targeted analytical comparison of a heated tobacco product aerosol against mainstream cigarette smoke: does heating tobacco produce an inherently different set of aerosol constituents?

Smoking-related diseases remain a significant public health concern, and heated tobacco products (HTPs) have emerged as a potential alternative to cigarettes. While several studies have confirmed that HTP aerosols contain lower levels of harmful and potentially harmful constituents (HPHCs) than cigarette smoke, less is known about constituents that are intrinsically higher in HTP aerosols. This study provides a comprehensive comparative assessment of an HTP aerosol produced with Tobacco Heating System 2.2 (THS) and comparator cigarette (CC) smoke aiming at identifying all unique or increased compounds in THS aerosol by applying a broad set of LC-MS and GC × GC-MS methods. To focus on differences due to heating versus burning tobacco, confounding factors were minimized by using the same tobacco in both test items and not adding flavorants. Of all analytical features, only 3.5%-corresponding to 31 distinctive compounds-were significantly more abundant in THS aerosol than in CC smoke. A notable subset of these compounds was identified as reaction products of glycerol. The only compound unique to THS aerosol was traced back to its presence in a non-tobacco material in the test item and not a direct product of heating tobacco. Our results demonstrate that heating a glycerol-containing tobacco substrate to the temperatures applied in THS does not introduce new compounds in the resulting aerosol compared to CC smoke which are detectable with the method portfolio applied in this study. Overall, this study contributes to a better understanding of the chemical composition of HTP aerosols and their potential impact on human health.

Comprehensive reversed-phase×chiral two-dimensional liquid chromatography coupled to quadrupole-time-of-flight tandem mass spectrometry with post-first dimension flow splitting for untargeted enantioselective amino acid analysis.

This work describes a comprehensive achiral × chiral two-dimensional liquid chromatography separation for enantioselective amino acid analysis coupled to electrospray ionization-tandem mass spectrometry detection using data-independent acquisition. Flow splitting after the first and second dimension separation was utilized for volumetric flow reduction and for enabling a multi-detector approach (with ultraviolet, fluorescence, charged aerosol, and MS detection), respectively. Derivatization with 6-aminoquinolyl-N-hydroxysuccinimidyl carbamate provided a chromophore, a fluorophore, and an efficient mass tag for efficient ionization in positive electrospray ionization-mass spectrometry. Chiral columns often have limitations in terms of their chemoselectivity, which may be a problem when complex sample mixtures with structurally related compounds need to be separated. It can be alleviated by a reversed-phase×chiral two-dimensional-liquid chromatography setup, in which the first dimension provides the chemoselectivity and a chiral tandem column constituted of quinine-carbamate derived weak anion-exchanger and zwitterionic ion-exchanger in the second dimension separation of D- and L-amino acid enantiomers. The method was used to control the stereointegrity of the therapeutic peptide octreotide. After hydrolysis, all amino acid constituents were detected with the correct configuration and composition. Some options for flow splitting and integration of destructive detectors in the first dimension separation are outlined.

Oral nicotine pouches with an aftertaste? Part 1: screening and initial toxicological assessment of flavorings and other ingredients.

Nicotine pouches are oral products that deliver nicotine without containing tobacco. Previous studies mainly focused on the determination of known tobacco toxicants, while yet no untargeted analysis has been published on unknown constituents, possibly contributing to toxicity. Furthermore, additives might enhance product attractiveness. We therefore performed an aroma screening with 48 different nicotine-containing and two nicotine-free pouches using gas chromatography coupled to mass spectrometry, following acidic and basic liquid-liquid extraction. For toxicological assessment of identified substances, European and international classifications for chemical and food safety were consulted. Further, ingredients listed on product packages were counted and grouped by function. Most abundant ingredients comprised sweeteners, aroma substances, humectants, fillers, and acidity regulators. 186 substances were identified. For some substances, acceptable daily intake limits set by European Food Safety Agency (EFSA) and Joint FAO/WHO Expert Committee on Food Additives are likely exceeded by moderate pouch consumption. Eight hazardous substances are classified according to the European CLP regulation. Thirteen substances were not authorized as food flavorings by EFSA, among them impurities such as myosmine and ledol. Three substances were classified by International Agency for Research on Cancer as possibly carcinogenic to humans. The two nicotine-free pouches contain pharmacologically active ingredients such as ashwagandha extract and caffeine. The presence of potentially harmful substances may point to the need for regulation of additives in nicotine-containing and nicotine-free pouches that could be based on provisions for food additives. For sure, additives may not pretend positive health effects in case the product is used.

Untargeted Metabolomics Approach Using UHPLC-HRMS to Unravel the Impact of Fermentation on Color and Phenolic Composition of Rosé Wines.

Color is a major quality trait of rosé wines due to their packaging in clear glass bottles. This color is due to the presence of phenolic pigments extracted from grapes to wines and products of reactions taking place during the wine-making process. This study focuses on changes occurring during the alcoholic fermentation of Syrah, Grenache and Cinsault musts, which were conducted at laboratory (250 mL) and pilot (100 L) scales. The color and phenolic composition of the musts and wines were analyzed using UV-visible spectrophotometry, and metabolomics fingerprints were acquired by ultra-high performance liquid chromatography-high-resolution mass spectrometry. Untargeted metabolomics data highlighted markers of fermentation stage (must or wine) and markers related to the grape variety (e.g., anthocyanins in Syrah, hydroxycinnamates and tryptophan derivatives in Grenache, norisoprenoids released during fermentation in Cinsault). Cinsault wines contained higher molecular weight compounds possibly resulting from the oxidation of phenolics, which may contribute to their high absorbance values.

IDSL.IPA Characterizes the Organic Chemical Space in Untargeted LC/HRMS Data Sets.

Generating comprehensive and high-fidelity metabolomics data matrices from LC/HRMS data remains to be extremely challenging for population-scale large studies (n > 200). Here, we present a new data processing pipeline, the Intrinsic Peak Analysis (IDSL.IPA) R package (https://ipa.idsl.me), to generate such data matrices specifically for organic compounds. The IDSL.IPA pipeline incorporates (1) identifying potential 12C and 13C ion pairs in individual mass spectra; (2) detecting and characterizing chromatographic peaks using a new sensitive and versatile approach to perform mass correction, peak smoothing, baseline development for local noise measurement, and peak quality determination; (3) correcting retention time and cross-referencing peaks from multiple samples by a dynamic retention index marker approach; (4) annotating peaks using a reference database of m/z and retention time; and (5) accelerating data processing using a parallel computation of the peak detection and alignment steps for larger studies. This pipeline has been successfully evaluated for studies ranging from 200 to 1600 samples. By specifically isolating high quality and reliable signals pertaining to carbon-containing compounds in untargeted LC/HRMS data sets from larger studies, IDSL.IPA opens new opportunities for discovering new biological insights in the population-scale metabolomics and exposomics projects. The package is available in the R CRAN repository at https://cran.r-project.org/package=IDSL.IPA.

The untargeted urine volatilome for biomedical applications: methodology and volatilome database.

Chemically diverse in compounds, urine can give us an insight into metabolic breakdown products from foods, drinks, drugs, environmental contaminants, endogenous waste metabolites, and bacterial by-products. Hundreds of them are volatile compounds; however, their composition has never been provided in detail, nor has the methodology used for urine volatilome untargeted analysis. Here, we summarize key elements for the untargeted analysis of urine volatilome from a comprehensive compilation of literature, including the latest reports published. Current achievements and limitations on each process step are discussed and compared. 34 studies were found retrieving all information from the urine treatment to the final results obtained. In this report, we provide the first specific urine volatilome database, consisting of 841 compounds from 80 different chemical classes.

An optimization method for untargeted MS-based isotopic tracing investigations of metabolism.

INTRODUCTION: Stable isotope tracer studies are increasingly applied to explore metabolism from the detailed analysis of tracer incorporation into metabolites. Untargeted LC/MS approaches have recently emerged and provide potent methods for expanding the dimension and complexity of the metabolic networks that can be investigated. A number of software tools have been developed to process the highly complex MS data collected in such studies; however, a method to optimize the extraction of valuable isotopic data is lacking. OBJECTIVES: To develop and validate a method to optimize automated data processing for untargeted MS-based isotopic tracing investigations of metabolism. METHODS: The method is based on the application of a suitable reference material to rationally perform parameter optimization throughout the complete data processing workflow. It was applied in the context of 13C-labelling experiments and with two different software, namely geoRge and X13CMS. It was illustrated with the study of a E. coli mutant impaired for central metabolism. RESULTS: The optimization methodology provided significant gain in the number and quality of extracted isotopic data, independently of the software considered. Pascal triangle samples are well suited for such purpose since they allow both the identification of analytical issues and optimization of data processing at the same time. CONCLUSION: The proposed method maximizes the biological value of untargeted MS-based isotopic tracing investigations by revealing the full metabolic information that is encoded in the labelling patterns of metabolites.

Reference materials for MS-based untargeted metabolomics and lipidomics: a review by the metabolomics quality assurance and quality control consortium (mQACC).

INTRODUCTION: The metabolomics quality assurance and quality control consortium (mQACC) is enabling the identification, development, prioritization, and promotion of suitable reference materials (RMs) to be used in quality assurance (QA) and quality control (QC) for untargeted metabolomics research. OBJECTIVES: This review aims to highlight current RMs, and methodologies used within untargeted metabolomics and lipidomics communities to ensure standardization of results obtained from data analysis, interpretation and cross-study, and cross-laboratory comparisons. The essence of the aims is also applicable to other 'omics areas that generate high dimensional data. RESULTS: The potential for game-changing biochemical discoveries through mass spectrometry-based (MS) untargeted metabolomics and lipidomics are predicated on the evolution of more confident qualitative (and eventually quantitative) results from research laboratories. RMs are thus critical QC tools to be able to assure standardization, comparability, repeatability and reproducibility for untargeted data analysis, interpretation, to compare data within and across studies and across multiple laboratories. Standard operating procedures (SOPs) that promote, describe and exemplify the use of RMs will also improve QC for the metabolomics and lipidomics communities. CONCLUSIONS: The application of RMs described in this review may significantly improve data quality to support metabolomics and lipidomics research. The continued development and deployment of new RMs, together with interlaboratory studies and educational outreach and training, will further promote sound QA practices in the community.

Distinguish oral-source VOCs and control their potential impact on breath biomarkers.

By means of glass bottle sampling followed by solid-phase microextraction gas chromatography-mass spectrometry (SPME-GC-MS) technique, the change characteristics of volatile organic compounds (VOCs) in breaths, between before gargling and after gargling, were investigated, respectively, in 41 healthy subjects and 50 esophageal cancer patients. Using an untargeted strategy, 143 VOC chromatographic peaks were enrolled in the statistical analysis. Based on the orthogonal partial least squares discriminant analysis (OPLS-DA), the VOC variations after gargling for each breath test group were obtained according to the combined criteria of variable importance in projection (VIP > 1.5), Wilcoxon signed-rank test (P < 0.05), and fold change (FC > 2.0). When gargled, the levels of indole, phenol, 1-propanol, and p-cresol in the breath of healthy people decreased; meanwhile, for esophageal cancer patients, the declined VOCs in breath were indole, phenol, dimethyl disulfide, and p-cresol. Particularly, these substances were previously reported as breath biomarkers in some diseases such as esophageal, gastric, thyroid, breast, oral, and lung cancers, as well as certain non-cancer disorders. The present work indicates that expiratory VOCs involve the prominent oral cavity source, and in the breath biomarkers study, the potential impact that originates from oral volatiles should be considered. In view of the present results, it is also proposed that gargle pretreatment could eliminate possible interference from the oral cavity VOCs that might benefit breath biomarker investigation. Gargle pretreatment helps to distinguish oral-source VOCs and control their potential impact on breath biomarkers.

Untargeted and targeted analysis of sarin poisoning biomarkers in rat urine by liquid chromatography and tandem mass spectrometry.

Chemical warfare agents continue to pose a real threat to humanity, despite their prohibition under the Chemical Weapons Convention. Sarin is one of the most toxic and lethal representatives of nerve agents. The methodology for the targeted analysis of known sarin metabolites has reached great heights, but little attention has been paid to the untargeted analysis of biological samples of victims exposed to this deadly poisonous substance. At present, the development of computational and statistical methods of analysis offers great opportunities for finding new metabolites or understanding the mechanisms of action or effect of toxic substances on the organism. This study presents the targeted LC-MS/MS determination of methylphosphonic acid and isopropyl methylphosphonic acid in the urine of rats exposed to a non-lethal dose of sarin, as well as the untarget urine analysis by LC-HRMS. Targeted analysis of polar acidic sarin metabolites was performed on a mixed-mode reversed-phase anion-exchange column, and untargeted analysis on a conventional reversed-phase C18 column. Isopropyl methylphosphonic acid was detected and quantified within 5 days after subcutaneous injection of sarin at a dose of 1/4 LD50. A combination of generalized additive mixed models and dose-response analysis with database searches using accurate mass of precursor ions and corresponding MS/MS spectra enabled us to propose new six potential biomarkers of biological response to exposure. The results confirm the well-known fact that sarin poisoning has a significant impact on the victims' metabolome, with inhibition of acetylcholinesterase being just the first step and trigger of the complex toxicodynamic response.

Profiling and quantitative analysis of underivatized fatty acids in Chlorella vulgaris microalgae by liquid chromatography-high resolution mass spectrometry.

Chlorella vulgaris is a popular microalga used for biofuel production; nevertheless, it possesses a strong cell wall that hinders the extraction of molecules, especially lipids within the cell wall. For tackling this issue, we developed an efficient and cost-effective method for optimal lipid extraction. Microlaga cell disruption by acid hydrolysis was investigated comparing different temperatures and reaction times; after hydrolysis, lipids were extracted with n-hexane. The best recoveries were obtained at 140°C for 90 min. The microalgae were then analyzed by an untargeted approach based on liquid chromatography with high-resolution mass spectrometry, providing the tentative identification of 28 fatty acids. First, a relative quantification on the untargeted data was performed using peak area as a surrogate of analyte abundance. Then, a targeted quantitative method was validated for the tentatively identified fatty acids, in terms of recovery (78-100%), intra- and interday relative standard deviations (<10 and <9%, respectively) and linearity (R2  > 0.98). The most abundant fatty acids were palmitic, palmitoleic, oleic, linoleic, linolenic, and stearic acids.

Thin layer chromatography based extraction approaches for improved analysis of volatile compounds with gas chromatography-mass spectrometry and direct analysis with gas analyzer.

In this study, thin-layer chromatography was applied for selective extraction of volatile compounds in perfume prior to analysis with solid phase microextraction and gas chromatography-mass spectrometry. The standard compounds were desorbed from the thin-layer chromatography plate and extracted at 80°C for 15 min showing good linearity of the calibration curves (R2  > 0.98) and acceptable recovery range (65-81%). The plate after the separation was cut into four smaller parts followed by solid phase microextraction/gas chromatography-mass spectrometry analysis, which revealed different compound profile in each part with the correlation between log P of the standard compounds and their positions along the thin-layer chromatography plate (R2  = 0.65). This approach was applied to analyze perfume compounds in the sample with strong matrix interference from the synthetic agarwood. Terpene hydrocarbons (woody-based odors), ketones/esters, aldehydes, ethers, and alcohols were mostly observed at 8 ± 1, 6 ± 1, 5 ± 2, 4 ± 2, and 3 ± 2 cm, respectively, from the bottom of the thin-layer chromatography plate. While, the conventional solid phase microextraction/gas chromatography-mass spectrometry analysis of this sample solution revealed only 62 compounds (including 35 perfume compounds), the four-piece approach resulted in 109 compounds (62 perfume compounds). Furthermore, the capability of thin-layer chromatography-Gas Analyzer approach to analyze the isomers in this complex sample was demonstrated.

Practical Considerations in Method Development for Gas Chromatography-Based Metabolomic Profiling.

This chapter discusses the fundamentals of gas chromatography (GC) to improve method development for metabolic profiling of complex biological samples. The selection of column geometry and phase ratio impacts analyte mass transfer, which must be carefully optimized for fast analysis. Stationary phase selection is critical to obtain baseline resolution of critical pairs, but such selection must consider important aspects of metabolomic protocols, such as derivatization and dependence of analyte identification on existing databases. Sample preparation methods are also addressed depending on the sample matrix, including liquid-liquid extraction and solid-phase microextraction.

Deep Learning Based Prediction of Gas Chromatographic Retention Indices for a Wide Variety of Polar and Mid-Polar Liquid Stationary Phases.

Prediction of gas chromatographic retention indices based on compound structure is an important task for analytical chemistry. The predicted retention indices can be used as a reference in a mass spectrometry library search despite the fact that their accuracy is worse in comparison with the experimental reference ones. In the last few years, deep learning was applied for this task. The use of deep learning drastically improved the accuracy of retention index prediction for non-polar stationary phases. In this work, we demonstrate for the first time the use of deep learning for retention index prediction on polar (e.g., polyethylene glycol, DB-WAX) and mid-polar (e.g., DB-624, DB-210, DB-1701, OV-17) stationary phases. The achieved accuracy lies in the range of 16-50 in terms of the mean absolute error for several stationary phases and test data sets. We also demonstrate that our approach can be directly applied to the prediction of the second dimension retention times (GC × GC) if a large enough data set is available. The achieved accuracy is considerably better compared with the previous results obtained using linear quantitative structure-retention relationships and ACD ChromGenius software. The source code and pre-trained models are available online.

Comparison of Targeted and Untargeted Approaches in Breath Analysis for the Discrimination of Lung Cancer from Benign Pulmonary Diseases and Healthy Persons.

The aim of the present study was to compare the efficiency of targeted and untargeted breath analysis in the discrimination of lung cancer (Ca+) patients from healthy people (HC) and patients with benign pulmonary diseases (Ca-). Exhaled breath samples from 49 Ca+ patients, 36 Ca- patients and 52 healthy controls (HC) were analyzed by an SPME-GC-MS method. Untargeted treatment of the acquired data was performed with the use of the web-based platform XCMS Online combined with manual reprocessing of raw chromatographic data. Machine learning methods were applied to estimate the efficiency of breath analysis in the classification of the participants. Results: Untargeted analysis revealed 29 informative VOCs, from which 17 were identified by mass spectra and retention time/retention index evaluation. The untargeted analysis yielded slightly better results in discriminating Ca+ patients from HC (accuracy: 91.0%, AUC: 0.96 and accuracy 89.1%, AUC: 0.97 for untargeted and targeted analysis, respectively) but significantly improved the efficiency of discrimination between Ca+ and Ca- patients, increasing the accuracy of the classification from 52.9 to 75.3% and the AUC from 0.55 to 0.82. Conclusions: The untargeted breath analysis through the inclusion and utilization of newly identified compounds that were not considered in targeted analysis allowed the discrimination of the Ca+ from Ca- patients, which was not achieved by the targeted approach.

Optimization of thin film solid phase microextraction and data deconvolution methods for accurate characterization of organic compounds in produced water.

The continued rise in the extraction of unconventional oil and gas across the globe poses many questions about how to manage these relatively new waste-streams. Produced water, the primary waste by-product, contains a diverse number of anthropogenic additives together with the numerous hydrocarbons extracted from the well. Due to potential environmental hazards, it is critical to characterize the chemical composition of this type of waste before proper disposal or remediation/reuse. In this work, a thin film solid phase microextraction approach was developed and optimized to characterize produced water. The thin film device consisted of hydrophilic-lipophilic balance particles embedded in polydimethylsiloxane and immobilized on a carbon mesh surface. These devices were chosen to provide broad extraction coverage and high reusability. Various parameters were evaluated to ensure reproducible results while minimizing analyte loss. This optimized protocol, consisting of a 15 min extraction followed by a short (3 s) rinsing step, enabled the reproducible analysis of produced water without any sample pretreatment. Extraction efficiency was suitable for both produced water additives and hydrocarbons. The developed approach was able to tentatively identify a total of 201 compounds from produced water samples, by using one-dimensional gas chromatography hyphenated to mass spectrometry and data deconvolution.

Investigating aroma diversity combining purge-and-trap, comprehensive two-dimensional gas chromatography, and mass spectrometry.

Headspace gas chromatography is frequently used for aroma profiling thanks to its ability to naturally exploit the volatility of aroma compounds, and also to provide chemical information on sample composition. Its main advantages rely on simplicity, no use of solvent, amenability to automation, and the cleanliness of the extract. In the present contribution, the most effective sampling (dynamic extraction), separation (multidimensional gas chromatography), and detection (mass spectrometry) techniques for untargeted analysis are exploited in combination, showing their potential in unraveling aroma profiles in fruit beers. To complete the overall analytical process, a neat workflow for data analysis is discussed and used for the successful characterization and identification of five different beer flavors (berries, cherry, banana, apple, and peach). From the technical viewpoint, the coupling of purge-and-trap, comprehensive two-dimensional gas chromatography, and mass spectrometry makes the global methodology unique, and it is for the first time discussed. A (low-)flow modulation approach allowed for the full transfer into the second dimension with mass-spectrometry compatible flow (< 7 mL/min), avoiding the need of splitting before detection and making the overall method sensitive (1.2-5.2-fold higher signal to noise ratio compared to unmodulated gas chromatography conditions) and selective.