Characterization of the key aroma compounds in Baojing Huangjincha 1 green tea through molecular docking and molecular sensory science.

Chlorine contaminants in postconsumer plastic pyrolysis oils are a major bottleneck to their further valorization in the steam cracking process, where typical feed specifications require less than 3 ppm of chlorine. Current chromatographic approaches lack sufficient elemental selectivity and compound-independent quantification for complex pyrolysis matrices. Here, we present the first chlorine-selective, quantitative, comprehensive two-dimensional gas chromatography method with atomic emission detection (GC×GC-AED) for the analysis of complex pyrolysis oils. AED parameters were systematically optimized for maximum selectivity and sensitivity to chlorine. AED exhibited a largely compound-independent response with <5% variability relative to the internal standard, 3-chlorothiophene, across 28 representative model compounds. This comprehensive technique enabled the selective detection and quantification of chlorine-containing compounds in five distinct pyrolysis oils derived from real polyvinyl chloride (PVC) waste and two from mixed plastic waste rich in polyolefins. Using this approach, 85-99% of detected chlorine compounds could be tentatively identified, and GC×GC-AED provided chlorine concentrations for each compound. The total chlorine content determined by combustion microcoulometry, X-ray fluorescence (XRF), and GC×GC-AED showed close agreement (<5% relative variation), highlighting the comprehensiveness and analytical reliability of the optimized AED method. This capability establishes GC×GC-AED as a powerful tool for understanding chlorine transformation pathways and for improving dechlorination strategies in waste-plastic valorization by mapping chlorine's fate throughout the process.

Comprehensive two-dimensional gas chromatography in environmental non-targeted analysis

Comparison of solid phase microextraction coatings for headspace extraction of volatile perfluoroalkyl substances using one-dimensional and comprehensive two-dimensional gas chromatography.

Fluorinated ionic liquids as gas chromatographic stationary phases for the separation of volatile per- and polyfluoroalkyl substances.

One-dimensional gas chromatography coupled with time-of-flight mass spectrometry (1D GC-TOF/MS) and comprehensive two-dimensional gas chromatography coupled with TOF/MS (GC×GC-TOF/MS) were evaluated as complementary platforms for diesel-fuel compositional profiling. Across all samples, 1D GC-TOF/MS reported 1,850 library-annotated features (similarity score ≥ 700), whereas GC×GC-TOF/MS reported 27,146 deconvoluted features, comprising 8,108 annotated features and 19,038 unnamed Peak-ID features. Only 595 annotated features were common to both workflows, yet workflow-specific statistical evaluation and PCA of GC×GC feature-occurrence profiles consistently highlighted the same atypical samples. Broad chemical grouping of annotated and predicted GC×GC features showed that paraffinic and olefinic/naphthenic hydrocarbon families dominated most fuels. In contrast, sample 51 × 57, and to a lesser extent K8LZ1 depending on feature inclusion, exhibited redistributed class patterns and a higher proportion of oxygenated or FAME-associated features. Integrating chromatographic profiling with routine physicochemical descriptors indicated that meaningful compositional divergence may occur even within a narrow specification window. Overall, 1D GC-TOF/MS remains effective for robust comparative screening, while GC×GC-TOF/MS provides additional selectivity and peak capacity to interrogate compositionally congested diesel matrices and to support future chemometric modelling.

To address the challenge of comparing entire chromatograms, three tile-based comparative analysis methods (Fisher ratio, pairwise, and relative variance ranking) were applied to data collected by comprehensive two-dimensional gas chromatography with vacuum ultraviolet spectroscopy detection (GC × GC-VUV). The tile-based scheme, originally developed for time-of-flight mass spectrometry (TOFMS) detection, was adapted herein to rank analytes on a hit list according to the metric for each method. We analyzed a GC × GC-VUV data set comprising 14 gas oils from five sample classes: straight-run (SR), light cycle oil (LCO), coker (CK), hydroconverted (HDC), and hydrotreated (HDT). Three replicates each of HDT and LCO gas oil GC × GC-VUV data were analyzed by Fisher ratio analysis with 459 out of 557 analyte hits exhibiting at least a 2-fold change, which were visualized by 2D chromatogram projections. Additionally, multivariate curve resolution-alternating least-squares was applied to obtain the pure VUV spectrum of each analyte, which was readily classified into five compound classes (saturates, olefins, mono-, di-, and triaromatics) and projected onto the 2D separation space to visualize the positions of these compound classes. Next, pairwise analysis, either between replicates of the same sample or between samples within the same sample class was studied. A concentration ratio >1.2 was determined as the 99% confidence level threshold for distinguishing analyte hits from background variation. Finally, relative variance analysis of all 24 gas oil chromatograms combined with principal component analysis enabled unsupervised differentiation of the five gas oil sample classes.

Online combustionof analytes through gas chromatography-combustion-isotoperatio mass spectrometry (GC-C-IRMS) has facilitated compound-specificisotope analysis (CSIA) for various applications like tracing environmentalcontamination and doping in sports. However, GC-C-IRMS criticallydepends on complete peak separation, where current combustion tubeswith an inner diameter (i.d.) of 0.5 mm and threaded nickel wiresare a notorious source of peak broadening. To reduce the reactor volumeby 3–6 fold, we instead produced Ni wall-coatings in catalyticmicroreactors of alumina and quartz (0.2 mm–0.3 mm i.d.). Electrolessplating coated the tube wall over a length of 18–20 cm coveringthe hot zone of the furnace. Coating thickness, characterized by scanningelectron microscopy, was (1.23 ± 0.12 μm, n = 7) and the total mass of deposited Ni was 0.10 mg/cm. A seed oxidationof 2 min before each chromatographic run facilitated accurate carbonand nitrogen isotope analysis of caffeine: Δδ13C = −0.16‰ ± 0.12‰ (quartz capillary, i.d.0.30 mm, splitless injection, n = 232); Δδ13C of 0.05‰ ± 0.09‰ and −0.16‰± 0.13‰ (two replicate alumina reactors, i.d. 0.20 mm,split injection, n = 336 and 347 respectively) andΔδ15N = 0.23‰ ± 0.22‰ (aluminareactor, i.d. 0.20 mm, split injection, n = 29).Increasing the residence time in the reactor enabled even accurateisotope analysis of atrazine (Δδ13C = 0.03‰± 0.21‰, n = 30; Δδ15N = −0.05‰± 0.15‰, n = 15) as a structure notoriouslyresistant to complete oxidation. A proof-of-principle test with n-alkanes demonstrated a 10-fold higher sensitivity and2-times better peak resolution compared to the current commercialconfiguration. Hence, our setup paves the path to developments forwhich sensitivity and peak width are critical like environmental traceanalysis and comprehensive two-dimensional gas chromatography (GCxGC-C-IRMS).

BackgroundThe ketogenic diet is being explored as an adjuvant intervention in breast cancer because it lowers circulating glucose and elevates ketone bodies such as β-hydroxybutyrate (BHB), but how individual ER+ breast cancer subtypes adapt to these conditions remains poorly characterized. We examined metabolic responses to BHB supplementation under glucose restriction in two ER+ breast cancer cell lines, asking whether metabolic adaptation patterns differ between models.MethodsMCF-7 and T47D cells were cultured under high glucose, glucose-restricted (5% of standard), or glucose-restricted with 10 mM BHB conditions and profiled by comprehensive two-dimensional gas chromatography–mass spectrometry (GC×GC-MS). Pairwise Welch’s t-tests with Benjamini–Hochberg false discovery rate (FDR) correction were applied to identify treatment-responsive metabolites. Targeted assays quantified intracellular glycine, SHMT1 protein, and total branched-chain amino acid (BCAA) concentrations across a BHB dose range (2.5-15 mM). Patient tumor transcriptomic data from TCGA (n=1,084) and paired tumor-normal samples from GSE58135 (n=20) were analyzed for genes involved in one-carbon, ketone body, and BCAA metabolism.ResultsMCF-7 and T47D cells exhibited markedly divergent metabolic responses to BHB. In MCF-7 cells, BHB supplementation produced a broad pattern-level metabolic shift: 75% of detected metabolites trended upward when BHB was added to glucose-restricted cultures (C vs. B comparison), with 1,4-butanediol reaching nominal significance (FC=2.35, p=0.016) and a 4.1-fold trend increase in lactic acid (p=0.11), although no individual metabolite survived FDR correction. T47D cells showed essentially no metabolic response to BHB at the global level. Targeted assays detected an elevation in glycine at 5 mM BHB in both cell lines that did not follow a monotonic dose response and was not accompanied by changes in SHMT1 protein expression. Total BCAA levels were elevated by BHB in T47D cells but remained unchanged in MCF-7 cells. In paired patient samples, OXCT1 (log2FC = −1.41), SHMT1 (log2FC = −1.31), and ACAT1 (log2FC = −1.07) were significantly downregulated in ER+ tumors relative to matched normal tissue (adjusted p < 0.001 for all three).ConclusionsER+ breast cancer cell lines show heterogeneous metabolic responses to BHB supplementation under glucose restriction. The broad pattern of metabolite elevation in MCF-7 but not T47D cells suggests that capacity to utilize ketone bodies as metabolic substrate varies between ER+ models. The downregulation of OXCT1, ACAT1, and SHMT1 in ER+ tumors compared to normal tissue identifies these enzymes as candidate biomarkers that may help stratify which patients are likely to benefit from ketogenic interventions. Findings related to individual metabolites should be regarded as exploratory and require validation in larger, adequately powered cohorts.

A local predictive modelling approach for enhanced separation of critical volatile compounds with flow-modulated comprehensive two-dimensional gas chromatography.

The migration of intentionally and non-intentionally added substances (IAS/NIAS) from food packaging into foodstuffs presents a significant challenge to consumer health and food safety. Accurate and comprehensive identification of these chemical migrants is therefore paramount. This review systematically summarizes recent advances in the analytical workflows used to identify these migrants. We critically evaluate the latest developments in both gas chromatography coupled to mass spectrometry (GC-MS) and liquid chromatography coupled to high-resolution mass spectrometry (LC-HRMS). Special attention is given to cutting-edge techniques, such as comprehensive two-dimensional gas chromatography (GC×GC) for enhanced separation of complex mixtures, high-resolution filtering (HRF) for leveraging the dual advantages of gas chromatography coupled to high-resolution mass spectrometry (GC-HRMS) accurate mass measurements and conventional low-resolution spectral matching, and ion mobility spectrometry (IMS) for its unique ability to resolve isomers. Concurrently, we provide an in-depth critique of the evolving data analysis strategies, from conventional targeted analysis to the more comprehensive suspect and nontargeted screening approaches. The principles, advantages, and limitations of each workflow are discussed in the context of their application to food packaging materials. Then, the review dissects major bottlenecks, notably the scarcity of reference standards and comprehensive mass spectral libraries, which hinder confident identification. Looking forward, we highlight promising future directions, emphasizing that the synergistic integration of open-access mass spectral databases, adoption of novel analytical techniques, and machine learning-based molecular property prediction will facilitate the identification of IAS and NIAS in food packaging. In addition, integrating chemical analysis with bioassays will enable the prioritization of high-hazard chemicals, ultimately improving the safety evaluation of food packaging.

Extra virgin olive oil (EVOO) represents a high value-added food product due to its nutritional, functional, and sensory properties. At the same time, its economic relevance makes it highly susceptible to adulteration and mislabelling-fraudulent activities become, over time, more sophisticated and difficult to reveal-thus driving the demand for continuous analytical innovation. The present review critically summarizes the most relevant advances in analytical techniques for EVOO analysis reported between 2020 and 2025. The discussion encompasses recent developments in the analysis of volatile, semi-volatile, and non-volatile fractions, as well as contaminants, with particular emphasis on hyphenated chromatographic techniques, mass spectrometry systems, and emerging sensor-based platforms. Advances such as comprehensive two-dimensional gas chromatography, ambient and ambient-assisted ionization mass spectrometry, low-pressure gas chromatography, and integrated microsensing devices are highlighted for their potential to enhance analyte detectability, throughput, robustness, and eco-efficiency. Overall, such innovations are paving the way toward faster, greener, and more effective analytical workflows, in line with the principles of sustainable food analysis and contributing to stronger EVOO quality control.

Coffee is a highly complex and variable matrix, with volatile profiles shaped by multiple factors including botanical origin, climatic and soil conditions, post-harvest treatments, and roasting parameters. This variability generates complex chemical patterns, encompassing hundreds of volatile compounds from diverse chemical classes including pyrazines, furans, aldehydes, ketones, and terpenes. The resulting chemical dimensionality poses significant analytical challenges, making the accurate identification of characteristic compounds and reliable discrimination of coffee origins particularly difficult. In this study, we applied comprehensive two-dimensional gas chromatography (GC×GC) coupled with computer vision (CV) to address these challenges. The workflow begins with untargeted fingerprinting, capturing all detectable compounds in a feature template. Multiple sample chromatograms are then combined into composite class images, representing the typical chemical features of each origin while minimising individual variability, which enables rapid pairwise comparison of different origins. CV-based pairwise comparisons highlight differential peaks, which are integrated into a targeted template for subsequent peak extraction. Multivariate analyses then identify the key discriminant compounds driving origin differentiation. Post-processing strategies, such as ion-specific intensity mapping, further enhance interpretability, enabling visualisation of compositional differences across key chemical families. Overall, this GC×GC-CV workflow provides a robust, rapid, and visually intuitive platform for comprehensive chemical characterisation and origin classification of coffee, integrating untargeted and targeted analyses in a single framework.

Volatile sulfur compounds belong to the group of odor active compounds with very low sensory threshold levels and can influence the aroma of various products either in a positive or a negative way. Due to their high odor potency and their low sensory thresholds, it is critical to identify these substances in complex food matrices. A new instrumental setup based on the combination of a two-dimensional comprehensive gas chromatographic system with simultaneous dual detection based on a sulfur chemiluminescence detector and a time-of-flight mass spectrometer separation and identification was tested. Additionally, two different column configurations were tested and compared for further optimization of separation and selectivity. Three different roasted coffee samples were used to demonstrate the potential of this new instrumental configuration, keeping in mind the volatile and odor active fraction of roasted coffee being among one of the most complex volatile composition when comparing to other food and beverage products.

Comprehensive two-dimensional gas chromatography coupled with time-of-flight mass spectrometry (GC×GC-TOFMS) is a powerful technique suited for resolving complex mixtures of volatile organic compounds (VOCs). In this study, GC×GC-TOFMS was combined with an image-based chromatographic fingerprinting strategy to characterize the fecal volatilome of subjects affected by non-celiac gluten/wheat sensitivity (NCGWS) under dietary intervention. Fifty participants underwent a dietary protocol consisting of a gluten-free diet followed by gluten reintroduction while receiving probiotics or placebo. Headspace solid-phase microextraction (HS-SPME) conditions were specifically optimized to maximize the extraction coverage of volatiles, while a combined untargeted-targeted fingerprinting approach (UT fingerprinting) enabled the mapping of approximately 1000 volatile features in fecal samples. Chemometric analyses, including principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA), highlighted informative differences in VOC profiles induced by gluten reintroduction and probiotic administration. Around 270 VOCs were annotated, expanding the existing knowledge on the human fecal volatilome by 20%, predominantly comprising microbial fermentation-derived metabolites such as short-chain fatty acid esters, aldehydes, alcohols, terpenes, and aromatic hydrocarbons. PLS-DA models discriminated probiotic-treated individuals from placebo recipients with 89-90% accuracy, confirming the impact of probiotic supplementation on gut metabolic signatures. The integration of GC×GC-TOFMS and image-based chromatographic fingerprinting effectively captured subtle metabolic variations, demonstrating its suitability in biomarker discovery for dietary interventions and personalized nutrition studies.

Polyvinyl chloride(PVC), the world’s third most-producedplastic, contributes significantly to the presence of organochloridecompounds in pyrolysis oil, which can cause corrosion and other operationalchallenges in fuel applications. Therefore, this study investigatesthe hydrodechlorination (HDC) of PVC pyrolysis oil using commercialPd/C and homemade Pd/ZrO2 catalysts. The oil was producedfrom virgin PVC that was thermally pyrolyzed in two stages at 350°C (1 h) and 550 °C (1 h) in a semibatch reactor. The oil’scomposition and chlorine levels were analyzed using comprehensivetwo-dimensional gas chromatography (GC × GC) coupled with flameionization detection and atomic emission detection. The pyrolysisoil had a carbon range from C6 to C20. Two-stepthermal pyrolysis effectively removed most of the chlorine, yieldingan oil with a chlorine content of 316 ppm. Catalyst characterizationmethods, including H2-TPD, BET, SEM/EDS, XRD, and XPS,were employed to evaluate the structural and surface properties. Initially,HDC efficiency was tested on chlorobenzene as a model compound acrossdifferent catalysts, Pd-to-chlorinated compound ratios, reaction times,and temperatures. Under moderate conditions, the Pd/C catalysts showedsuperior performance due to better Pd dispersion than Pd/ZrO2, though they exhibited lower reusability. At elevated conditions,such as higher temperature or longer reaction time, 0.5%Pd/ZrO2 narrowed the gap in HDC performance compared to 5%Pd/C. TheHDC of the PVC pyrolysis oil followed a trend similar to that of themodel compounds, with overall chlorine removal efficiencies of 82–89%under optimized conditions. These findings highlight the potentialof Pd-based catalysts to reduce chlorine content in PVC pyrolysisoil, thereby enabling its use in cleaner fuel production.

Inflammation is associated with metabolic alterations that can lead to the release of volatile organic compounds (VOCs) reflecting cellular biochemical activity. Profiling these volatile metabolites may provide insight into cellular responses to inflammatory stimuli, although their characterization in skin-derived cells remains limited. In this exploratory proof-of-concept study, we investigated the volatile metabolite profiles of human skin fibroblasts exposed to different inflammatory stimuli. Fibroblast cell lines were stimulated with polyinosinic:polycytidylic acid (Poly I:C), tumor necrosis factor-alpha (TNF-α), and lipopolysaccharide (LPS) to model viral-, cytokine-, and bacterial-associated stress conditions. Headspace solid-phase microextraction coupled with comprehensive two-dimensional gas chromatography and time-of-flight mass spectrometry (HS-SPME-GC×GC-TOFMS) was applied to analyze volatile metabolites released from the cell cultures, enabling exploratory profiling of the fibroblast volatilome. A data-processing workflow including pairwise comparisons between experimental groups and statistical filtering was implemented to identify volatile features associated with the different conditions. Several VOCs were tentatively identified, mainly belonging to alcohol, ester, and hydrocarbon classes, and showed differential abundance patterns between stimulated and control samples. Multivariate analysis indicated a separation between stimulated and non-stimulated groups, suggesting stimulus-associated differences in the volatile profiles of fibroblast cultures. While these observations may reflect metabolic responses occurring under inflammatory stimulation, the chemical identity and biochemical origins of several detected features remain to be confirmed. All in all, this study demonstrates the feasibility of applying HS-SPME-GC×GC-TOFMS-based volatilome profiling to investigate stimulus-associated changes in fibroblast cultures. The detected VOC patterns should therefore be considered preliminary observations requiring further chemical characterization and independent validation. Future studies including larger sample numbers, complementary biological verification of the inflammatory response, and more physiologically relevant experimental models will be necessary to further assess the robustness and potential relevance of these volatile signatures in the context of inflammatory processes.

Rapid expansion of the plant-based milk market has increased the need to understand how the aroma profiles of these alternatives differ from that of dairy milk and how raw material selection and processing influence volatile formation. This study compared the volatile profiles of dairy milk, commercial plant-based milks, and laboratory-prepared cereal and pseudocereal milk prototypes to identify promising materials for plant-based milk development. Comprehensive two-dimensional gas chromatography time-of-flight mass spectrometry (GC×GC-TOFMS) combined with chemometric analysis was used to characterize volatile compounds in bovine milk, four commercial plant milks, and five laboratory-prepared plant milks. Dairy milk was characterized by fatty acids, esters, and other lipid-derived volatiles, whereas plant-based samples were associated with hydrocarbons, pyrazines, ketones, and phenols. Within the plant-based group, volatile differences were influenced by raw material type and processing history. Commercial products showed more evident processing-related features, whereas laboratory-prepared cereal samples exhibited a simpler volatile background. Among them, barley milk displayed a distinctive toasted and cereal-like signature. Overall, the selected cereal and pseudocereal matrices showed distinct volatile characteristics, as well as relatively uniform raw material backgrounds, implying greater flexibility in aroma expression. These features make them promising candidates for dairy alternatives and may help guide future plant-based milk formulation.

Volatilomics is an emerging discipline aimed at characterizing volatile metabolites in diverse matrices. Recently, comprehensive two-dimensional gas chromatography (GC × GC) coupled with parallel flame ionization detection (FID) and mass spectrometry (MS) has gained attention for combining accurate quantification with unambiguous compound identification. Traditionally, FID and MS data are processed independently. This study addresses their integration by merging FID and MS chromatograms into a single fused chromatogram, enhancing pattern recognition during template matching and enabling large-scale quantitative volatilomics. Feature matching is guided by MS spectral similarity, minimizing mismatches and extracting FID responses for robust quantification. The contribution discusses the workflow designed to obtain combined detector signals and the challenges posed by dual parallel detection operated under both thermal and differential-flow modulation, dual-parallel second-dimension column configurations, and differences in acquisition frequency between detectors. From an application standpoint, chromatogram fusion directly responds to emerging analytical needs in volatilomics-supporting quantitative, high-throughput analysis across extended time frames through the FID channel, while ensuring the mandatory MS confirmation required for the reliable identification of fragrance allergens and regulated compounds. The resulting fused chromatogram consolidates complementary detector information within a single multidimensional chromatogram, improving data consistency, interpretability, and throughput. Overall, chromatogram-level fusion represents a key step toward integrated, multimodal analytical platforms for robust and scalable volatilomics workflows.

Atmospheric intermediate volatile organic compounds （IVOCs） are complex mixtures. Due to the limited separation and identification capabilities of one-dimensional gas chromatography （1D-GC）， a large portion of atmospheric IVOCs-which often elute as unresolved or complex peaks-are categorized as an unspeciated complex mixture （UCM）. This constrains the accurate apportionment of atmospheric IVOCs. To address this problem， an alternative analytical method has been developed utilizing thermal desorption-flow modulation comprehensive two-dimensional gas chromatography coupled with time-of-flight mass spectrometry （TD-FM GC×GC-TOF MS） to measure atmospheric IVOCs. Analytical optimization focused on fill and flush time within a 3-second modulation period， with optimal values determined as 2 870 ms and 130 ms， respectively， to maximize separation efficiency and signal response. Compounds were separated on a ZB-5HT column （20 m×0.18 mm×0.18 μm） as the first dimension， with a BP-50 column （5 m×0.25 mm×0.20 μm） serving as the second dimension. Helium was employed as the carrier gas with column pressures maintained at 254.4 kPa （first dimension） and 202.1 kPa （second dimension）. The following temperature program was used： 50 ℃ for 10 min， then increased to 300 ℃ at 10 ℃/min， with the final temperature held for 20 min. The MS was operated in electron impact （EI， 70 eV） ionization mode. Both the ion source and transfer line temperatures were set at 300 ℃. MS signals were recorded in full scan mode with a scan range of m/z 40 to m/z 800. During thermal desorption， the desorption temperature was set at 320 ℃， and the cold trap temperature was maintained at -10 ℃ to capture desorbed targets. The secondary desorption flow rate was set at 16 mL/min. Under these optimized conditions， the method demonstrated acceptable linearity， with correlation coefficients ranging from 0.922 3 to 0.998 4， for 69 selected targets spanning 1 to 20 ng/tube， with method detection limits （spiked at 1 ng/tube） between 0.010 7 and 0.410 1 ng/m3， with recoveries ranging from 80.4% to 136.0%. Relative standard deviation values among replicate samples （n=7） spiked at 1 ng/tube， 3 ng/tube， and 10 ng/tube levels were around 4.5%-33.9%， 3.2%-19.9% and 3.5%-18.6%， respectively. Application of the method to atmospheric IVOC samples collected from an urban site of Shanghai， revealed total IVOCs mass concentrations of 8.6-61.1 μg/m3. Of these， mass concentrations of 853 individual species identified by the present method comprised 96.2% of total IVOCs. Consequently，the newly developed method improved the identification rate for UCM. Importantly， the newly speciated aromatics， chlorinated IVOCs， and oxygenated IVOCs exhibited different emission sources distinct from those routinely monitored compounds. Therefore， this study established a reliable method for determining atmospheric IVOCs， offering strong data support for precise source apportionment of complex IVOCs in the atmosphere. Additionally， this method can be used to monitor IVOCs across various environmental metrics， thereby providing essential data to support the management and control of these compounds.