Development and validation of a classification model for boar taint detection in pork fat samples

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.

Theoretical modeling and machine learning-based data processing workflows in comprehensive two-dimensional gas chromatography-A review

In recent years, comprehensive two-dimensional gas chromatography (GC × GC) has been gradually gaining prominence as a preferred method for the analysis of complex samples due to its higher peak capacity and resolution power compared to conventional gas chromatography (GC). Nonetheless, to fully benefit from the capabilities of GC × GC, a holistic approach to method development and data processing is essential for a successful and informative analysis. Method development enables the fine-tuning of the chromatographic separation, resulting in high-quality data. While generating such data is pivotal, it does not necessarily guarantee that meaningful information will be extracted from it. To this end, the first part of this manuscript reviews the importance of theoretical modeling in achieving good optimization of the separation conditions, ultimately improving the quality of the chromatographic separation. Multiple theoretical modeling approaches are discussed, with a special focus on thermodynamic-based modeling. The second part of this review highlights the importance of establishing robust data processing workflows, with a special emphasis on the use of advanced data processing tools such as, Machine Learning (ML) algorithms. Three widely used ML algorithms are discussed: Random Forest (RF), Support Vector Machine (SVM), and Partial Least Square-Discriminate Analysis (PLS-DA), highlighting their role in discovery-based analysis.

Tile-Based Random Forest Analysis for Analyte Discovery in Balanced and Unbalanced GC × GC-TOFMS Data Sets

In this study, we introduce a new nontargeted tile-based supervised analysis method that combines the four-grid tiling scheme previously established for the Fisher ratio (F-ratio) analysis (FRA) with the estimation of tile hit importance using the machine learning (ML) algorithm Random Forest (RF). This approach is termed tile-based RF analysis. As opposed to the standard tile-based F-ratio analysis, the RF approach can be extended to the analysis of unbalanced data sets, i.e., different numbers of samples per class. Tile-based RF computes out-of-bag (oob) tile hit importance estimates for every summed chromatographic signal within each tile on a per-mass channel basis (m/z). These estimates are then used to rank tile hits in a descending order of importance. In the present investigation, the RF approach was applied for a two-class comparison of stool samples collected from omnivore (O) subjects and stored using two different storage conditions: liquid (Liq) and lyophilized (Lyo). Two final hit lists were generated using balanced (8 vs Eight comparison) and unbalanced (8 vs Nine comparison) data sets and compared to the hit list generated by the standard F-ratio analysis. Similar class-distinguishing analytes (p < 0.01) were discovered by both methods. However, while the FRA discovered a more comprehensive hit list (65 hits), the RF approach strictly discovered hits (31 hits for the balanced data set comparison and 29 hits for the unbalanced data set comparison) with concentration ratios, [OLiq]/[OLyo], greater than 2 (or less than 0.5). This difference is attributed to the more stringent feature selection process used by the RF algorithm. Moreover, our findings suggest that the RF approach is a promising method for identifying class-distinguishing analytes in settings characterized by both high between-class variance and high within-class variance, making it an advantageous method in the study of complex biological matrices.

Comprehensive Insight into Colorectal Cancer Metabolites and Lipids for Human Serum: A Proof-of-Concept Study

Colorectal cancer (CRC) ranks as the third most frequently diagnosed cancer and the second leading cause of cancer-related deaths. The current endoscopic-based or stool-based diagnostic techniques are either highly invasive or lack sufficient sensitivity. Thus, there is a need for less invasive and more sensitive screening approaches. We, therefore, conducted a study on 64 human serum samples representing three different groups (adenocarcinoma, adenoma, and control) using cutting-edge GC×GC-LR/HR-TOFMS (comprehensive two-dimensional gas chromatography coupled with low/high-resolution time-of-flight mass spectrometry). We analyzed samples with two different specifically tailored sample preparation approaches for lipidomics (fatty acids) (25 μL serum) and metabolomics (50 μL serum). In-depth chemometric screening with supervised and unsupervised approaches and metabolic pathway analysis were applied to both datasets. A lipidomics study revealed that specific PUFA (ω-3) molecules are inversely associated with increased odds of CRC, while some PUFA (ω-6) analytes show a positive correlation. The metabolomics approach revealed downregulation of amino acids (alanine, glutamate, methionine, threonine, tyrosine, and valine) and myo-inositol in CRC, while 3-hydroxybutyrate levels were increased. This unique study provides comprehensive insight into molecular-level changes associated with CRC and allows for a comparison of the efficiency of two different analytical approaches for CRC screening using same serum samples and single instrumentation.

Top-Down Approach to Retention Time Prediction in Comprehensive Two-Dimensional Gas Chromatography-Mass Spectrometry

In this contribution, we describe a novel modeling approach to predicting retention times (t_r) in comprehensive two-dimensional gas chromatography coupled to time-of-flight mass spectrometry (GC × GC-ToF-MS) with a particular emphasis on the second-dimension (²D) retention time predictions (²t_r). This approach is referred to as a "top-down" approach in that it breaks down the complete GC × GC separation into two independent one-dimensional gas chromatography separations (1D-GC). In this regard, both dimensions, that is, first dimension (¹D) and second dimension (²D) are treated separately, and the cryogenic modulator is simply considered as a second consecutive injection device. Separate 1D-GC t_r predictions are performed on both dimensions using the same flow rate as the one deployed in the conventional GC × GC system. The separate t_r predictions are then combined to account for the two-dimensional separation. This model was applied to 24 analytes from 2 standard mixtures (Grob Test Mix and Fragrance Materials Test Mix) and assessed across 9 GC × GC chromatographic conditions. The experimental and predicted chromatographic retention space occupations were assessed by using the convex hull approach defined by the Delaunay triangulation. The predicted percentage of space occupation corresponded favorably with the experimental values. Furthermore, the top-down approach enabled an accurate prediction of the ²t_r of all investigated analytes, providing an average ²t_r modeling error of 0.26 ± 0.01 s.

Distinguishing between Decaffeinated and Regular Coffee by HS-SPME-GC×GC-TOFMS, Chemometrics, and Machine Learning

Coffee, one of the most popular beverages in the world, attracts consumers by its rich aroma and the stimulating effect of caffeine. Increasing consumers prefer decaffeinated coffee to regular coffee due to health concerns. There are some main decaffeination methods commonly used by commercial coffee producers for decades. However, a certain amount of the aroma precursors can be removed together with caffeine, which could cause a thin taste of decaffeinated coffee. To understand the difference between regular and decaffeinated coffee from the volatile composition point of view, headspace solid-phase microextraction two-dimensional gas chromatography time-of-flight mass spectrometry (HS-SPME-GC×GC-TOFMS) was employed to examine the headspace volatiles of eight pairs of regular and decaffeinated coffees in this study. Using the key aroma-related volatiles, decaffeinated coffee was significantly separated from regular coffee by principal component analysis (PCA). Using feature-selection tools (univariate analysis: t-test and multivariate analysis: partial least squares-discriminant analysis (PLS-DA)), a group of pyrazines was observed to be significantly different between regular coffee and decaffeinated coffee. Pyrazines were more enriched in the regular coffee, which was due to the reduction of sucrose during the decaffeination process. The reduction of pyrazines led to a lack of nutty, roasted, chocolate, earthy, and musty aroma in the decaffeinated coffee. For the non-targeted analysis, the random forest (RF) classification algorithm was used to select the most important features that could enable a distinct classification between the two coffee types. In total, 20 discriminatory features were identified. The results suggested that pyrazine-derived compounds were a strong marker for the regular coffee group whereas furan-derived compounds were a strong marker for the decaffeinated coffee samples.

Volatile organic compound profiling to explore primary graft dysfunction after lung transplantation

Primary graft dysfunction (PGD) is a major determinant of morbidity and mortality following lung transplantation. Delineating basic mechanisms and molecular signatures of PGD remain a fundamental challenge. This pilot study examines if the pulmonary volatile organic compound (VOC) spectrum relate to PGD and postoperative outcomes. The VOC profiles of 58 bronchoalveolar lavage fluid (BALF) and blind bronchial aspirate samples from 35 transplant patients were extracted using solid-phase-microextraction and analyzed with comprehensive two-dimensional gas chromatography coupled to time-of-flight mass spectrometry. The support vector machine algorithm was used to identify VOCs that could differentiate patients with severe from lower grade PGD. Using 20 statistically significant VOCs from the sample headspace collected immediately after transplantation (< 6 h), severe PGD was differentiable from low PGD with an AUROC of 0.90 and an accuracy of 0.83 on test set samples. The model was somewhat effective for later time points with an AUROC of 0.80. Three major chemical classes in the model were dominated by alkylated hydrocarbons, linear hydrocarbons, and aldehydes in severe PGD samples. These VOCs may have important clinical and mechanistic implications, therefore large-scale study and potential translation to breath analysis is recommended.

Deeper investigation of oxygen-containing compounds in oleaginous feedstock (animal fat) by preparative column chromatography and comprehensive two-dimensional gas chromatography coupled with high-resolution time-of-flight mass spectrometry

The production of renewable fuels as biodiesel and bio-jet fuel is usually originated by the transformation and processing of oleaginous feedstocks, mainly composed of triacylglycerols. Currently, a significant part of the triacylglycerol production relies on grassy oil crops or other woody oil plants, representing more than 120 million metric tons every year. Considering that the worldwide triacylglycerol demand is expected to rise in the future, alternative routes are necessary to ensure a sustainable biodiesel industry and limit diesel price volatility. In this context, the use of animal fats could be an interesting alternative for biodiesel production as the production of animal byproducts represents nearly 17 million tons per year in the European Union only (2020). Animal fats, however, contain large amounts of no-esterified fatty acids and other oxygen compounds, reducing the yield of biodiesel. Therefore, a specific pretreatment is needed before the trans-esterification process. The setup of such appropriate pretreatments requires detailed upstream characterization of the minor components present in the feedstock. For this purpose, the minor component profile of animal fat was investigated by comprehensive two-dimensional gas chromatography coupled with high-resolution time-of-flight mass spectrometry. This was preceded by an innovative sample fractionation and focalization of these minor components by a preparative liquid chromatographic column method. The overall method permitted to extract different levels of information from the two-dimensional chromatograms, leading to a tentative identification of more than 150 compounds, mainly oxygenated, belonging to different chemical classes.

Insights into Dodecenes Produced from Olefin Oligomerization Based on Two-Dimensional Gas Chromatography-Photoionization-Time of Flight Mass Spectrometry and Multivariate Statistics

Catalyzed light olefin oligomerization is widely used in petrochemical industries to produce fuels and chemicals. Light olefins such as propene and butenes are commonly selected as feedstocks. Solid phosphoric acid (SPA) and zeolite are representative acidic catalysts. Both the feedstocks and catalysts have an impact on the product composition. In this study, state-of-the-art instrumentation two-dimensional gas chromatography (GC × GC) coupled photoionization-time of flight mass spectrometry was employed to investigate the composition of dodecene products produced from olefin oligomerization. Information such as the olefin congener distribution, dodecene structural subgroup distribution, and individual dodecene isomers was obtained and utilized in the statistical analyses. By using specific data sets of the product composition, the distinguishment between SPA and zeolite catalysts as well as among the feedstocks was achieved by applying the unsupervised screening approaches (principal component analysis and hierarchical clustering analysis). The potential indicators of catalysts and feedstocks were selected by the feature selection methods (univariate analysis: analysis of variance and multivariate analysis: partial least squares-discriminant analysis).

Modeling approaches for temperature-programmed gas chromatographic retention times under vacuum outlet conditions

This contribution evaluates the performance of two predictive approaches in calculating temperature-programmed gas chromatographic retention times under vacuum outlet conditions. In the first approach, the predictions are performed according to a thermodynamic-based model, while in the second approach the predictions are conducted by using the temperature-programmed retention time equation. These modeling approaches were evaluated on 47 test compounds belonging to different chemical classes, under different experimental conditions, namely, two modes of gas flow regulation (i.e., constant inlet pressure and constant flow rate), and different temperature programs (i.e., 7 °C/min, 5 °C/min, and 3 °C/min). Both modeling approaches gave satisfactory results and were able to accurately predict the elution profiles of the studied test compounds. The thermodynamic-based model provided more satisfying results under constant flow rate mode, with average modeling errors of 0.43%, 0.33%, and 0.15% across all the studied temperature programs. Nevertheless, under constant inlet pressure mode, lower modeling errors were achieved when using the temperature-programmed retention time equation, with average modeling errors of 0.18%, 0.18%, and 0.31% across the used temperature programs.

Breathomics to diagnose systemic sclerosis using thermal desorption and comprehensive two-dimensional gas chromatography high-resolution time-of-flight mass spectrometry

Systemic sclerosis is a rare autoimmune disease associated with rapidly evolving interstitial lung disease, responsible for the disease severity and mortality. Specific biomarkers enabling the early diagnosis and prognosis associated with the disease progression are highly needed. Volatile organic compounds in exhaled breath are widely available and non-invasive and have the potential to reflect metabolic processes occurring within the body. Comprehensive two-dimensional gas chromatography coupled to high-resolution mass spectrometry was used to investigate the potential of exhaled breath to diagnose systemic sclerosis. The exhaled breath of 32 patients and 30 healthy subjects was analyzed. The high resolving power of this approach enabled the detection of 356 compounds in the breath of systemic sclerosis patients, which was characterized by an increase of mainly terpenoids and hydrocarbons. In addition, the use of 4 complementary statistical approaches (two-tailed equal variance t-test, fold change, partial least squares discriminant analysis, and random forest) resulted in the identification of 16 compounds that can be used to discriminate systemic sclerosis patients from healthy subjects. Receiver operating curves were generated that provided an accuracy of 90%, a sensitivity of 92%, and a specificity of 89%. The chemical identification of eight compounds predictive of systemic sclerosis was validated using commercially available standards. The analytical variations together with the volatile composition of room air were carefully monitored during the timeframe of the study to ensure the robustness of the technique. This study represents the first reported evaluation of exhaled breath analysis for systemic sclerosis diagnosis and provides surrogate markers for such disease.

Advanced mono- and multi-dimensional gas chromatography-mass spectrometry techniques for oxygen-containing compound characterization in biomass and biofuel samples

A wide variety of biomass, from triglycerides to lignocellulosic-based feedstock, are among promising candidates to possibly fulfill requirements as a substitute for crude oils as primary sources of chemical energy feedstock. During the feedstock processing carried out to increase the H:C ratio of the products, heteroatom-containing compounds can promote corrosion, thus limiting and/or deactivating catalytic processes needed to transform the biomass into fuel. The use of advanced gas chromatography techniques, in particular multi-dimensional gas chromatography, both heart-cutting and comprehensive coupled to mass spectrometry, has been widely exploited in the field of petroleomics over the past 30 years and has also been successfully applied to the characterization of volatile and semi-volatile compounds during the processing of biomass feedstock. This review intends to describe advanced gas chromatography-mass spectrometry-based techniques, mainly focusing in the period 2011-early 2020. Particular emphasis has been devoted to the multi-dimensional gas chromatography-mass spectrometry techniques, for the isolation and characterization of the oxygen-containing compounds in biomass feedstock. Within this context, the most recent advances to sample preparation, derivatization, as well as gas chromatography instrumentation, mass spectrometry ionization, identification, and data handling in the biomass industry, are described.

Multimodal combination of GC × GC-HRTOFMS and SIFT-MS for asthma phenotyping using exhaled breath

Chronic inflammatory lung diseases impact more than 300 million of people worldwide. Because they are not curable, these diseases have a high impact on both the quality of life of patients and the healthcare budget. The stability of patient condition relies mostly on constant treatment adaptation and lung function monitoring. However, due to the variety of inflammation phenotypes, almost one third of the patients receive an ineffective treatment. To improve phenotyping, we evaluated the complementarity of two techniques for exhaled breath analysis: full resolving comprehensive two-dimensional gas chromatography coupled to high-resolution time-of-flight mass spectrometry (GC × GC-HRTOFMS) and rapid screening selected ion flow tube MS (SIFT-MS). GC × GC-HRTOFMS has a high resolving power and offers a full overview of sample composition, providing deep insights on the ongoing biology. SIFT-MS is usually used for targeted analyses, allowing rapid classification of samples in defined groups. In this study, we used SIFT-MS in a possible untargeted full-scan mode, where it provides pattern-based classification capacity. We analyzed the exhaled breath of 50 asthmatic patients. Both techniques provided good classification accuracy (around 75%), similar to the efficiency of other clinical tools routinely used for asthma phenotyping. Moreover, our study provides useful information regarding the complementarity of the two techniques.

A benchmarking protocol for breath analysis: the peppermint experiment

Sampling of volatile organic compounds (VOCs) has shown promise for detection of a range of diseases but results have proved hard to replicate due to a lack of standardization. In this work we introduce the 'Peppermint Initiative'. The initiative seeks to disseminate a standardized experiment that allows comparison of breath sampling and data analysis methods. Further, it seeks to share a set of benchmark values for the measurement of VOCs in breath. Pilot data are presented to illustrate the standardized approach to the interpretation of results obtained from the Peppermint experiment. This pilot study was conducted to determine the washout profile of peppermint compounds in breath, identify appropriate sampling time points, and formalise the data analysis. Five and ten participants were recruited to undertake a standardized intervention by ingesting a peppermint oil capsule that engenders a predictable and controlled change in the VOC profile in exhaled breath. After collecting a pre-ingestion breath sample, five further samples are taken at 2, 4, 6, 8, and 10 h after ingestion. Samples were analysed using ion mobility spectrometry coupled to multi-capillary column and thermal desorption gas chromatography mass spectrometry. A regression analysis of the washout data was used to determine sampling times for the final peppermint protocol, and the time for the compound measurement to return to baseline levels was selected as a benchmark value. A measure of the quality of the data generated from a given technique is proposed by comparing data fidelity. This study protocol has been used for all subsequent measurements by the Peppermint Consortium (16 partners from seven countries). So far 1200 breath samples from 200 participants using a range of sampling and analytical techniques have been collected. The data from the consortium will be disseminated in subsequent technical notes focussing on results from individual platforms.

Comparison of the effect of chemically and biologically induced inflammation on the volatile metabolite production of lung epithelial cells by GC×GC-TOFMS

Exhaled breath analysis has a high potential for early non-invasive diagnosis of lung inflammatory diseases, such as asthma. The characterization and understanding of the inflammatory metabolic pathways involved into volatile organic compounds (VOCs) production could bring exhaled breath analysis into clinical practice and thus open new therapeutic routes for inflammatory diseases. In this study, lung inflammation was simulated in vitro using A549 epithelial cells. We compared the VOC production from A549 epithelial cells after a chemically induced oxidative stress in vitro, exposing the cells to H₂O₂, and a biological stress, exposing the cells to an inflammatory pool of sputum supernatants. Special attention was devoted to define proper negative and positive controls (8 different types) for our in vitro models, including healthy sputum co-culture. Sputum from 25 asthmatic and 8 healthy patients were collected to create each pool of supernatants. Each sample type was analyzed in 4 replicates using solid-phase microextraction (SPME) comprehensive two-dimensional gas chromatography hyphenated to time-of-flight mass spectrometry (GC×GC-TOFMS). This approach offers high resolving power for complex VOC mixtures. According to the type of inflammation induced, significantly different VOCs were produced by the epithelial cells compared to all controls. For both chemical and biological challenges, an increase of carbonyl compounds (54%) and hydrocarbons (31%) was observed. Interestingly, only the biological inflammation model showed a significant cell proliferation together with an increased VOC production linked to asthma airway inflammation. This study presents a complete GC×GC-TOFMS workflow for in vitro VOC analysis, and its potential to characterize complex lung inflammatory mechanisms.

Exhaled Volatile Organic Compounds Are Able to Discriminate between Neutrophilic and Eosinophilic Asthma

Rationale: Analysis of exhaled breath for asthma phenotyping using endogenously generated volatile organic compounds (VOCs) offers the possibility of noninvasive diagnosis and therapeutic monitoring. Induced sputum is indeed not widely available and markers of neutrophilic asthma are still lacking.Objectives: To determine whether analysis of exhaled breath using endogenously generated VOCs can be a surrogate marker for recognition of sputum inflammatory phenotypes.Methods: We conducted a prospective study on 521 patients with asthma recruited from the University Asthma Clinic of Liege. Patients underwent VOC measurement, fraction of exhaled nitric oxide (Fe_NO) spirometry, sputum induction, and gave a blood sample. Subjects with asthma were classified in three inflammatory phenotypes according to their sputum granulocytic cell count.Measurements and Main Results: In the discovery study, seven potential biomarkers were highlighted by gas chromatography-mass spectrometry in a training cohort of 276 patients with asthma. In the replication study (n = 245), we confirmed four VOCs of interest to discriminate among asthma inflammatory phenotypes using comprehensive two-dimensional gas chromatography coupled to high-resolution time-of-flight mass spectrometry. Hexane and 2-hexanone were identified as compounds with the highest classification performance in eosinophilic asthma with accuracy comparable to that of blood eosinophils and Fe_NO. Moreover, the combination of Fe_NO, blood eosinophils, and VOCs gave a very good prediction of eosinophilic asthma (area under the receiver operating characteristic curve, 0.9). For neutrophilic asthma, the combination of nonanal, 1-propanol, and hexane had a classification performance similar to Fe_NO or blood eosinophils in eosinophilic asthma. Those compounds were found in higher levels in neutrophilic asthma.Conclusions: Our study is the first attempt to characterize VOCs according to sputum granulocytic profile in a large population of patients with asthma and provide surrogate markers for neutrophilic asthma.

Compositional elucidation of heavy petroleum base oil by GC × GC-EI/PI/CI/FI-TOFMS

Comprehensive two-dimensional gas chromatography (GC × GC) coupled to time-of-flight mass spectrometry is a powerful separation tool for complex petroleum product analysis. However, the most commonly used electron ionization (EI) technique often makes the identification of the majority of hydrocarbons impossible due to the exhaustive fragmentation and lack of molecular ion preservation, prompting the need of soft-ionization energies. In this study, three different soft-ionization techniques including photo ionization (PI), chemical ionization (CI), and field ionization (FI) were compared against EI to elucidate their relative capabilities to reveal different base oil hydrocarbon classes. Compared with EI (70 eV), PI (10.8 eV) retained significant molecular ion (M^+· ) information for a large number of isomeric species including branched-alkanes and saturated monocyclic hydrocarbons along with unique fragmentation patterns. However, for bicyclic/polycyclic naphthenic and aromatic compounds, EI played upper hand by retaining molecular as well as fragment ions to identify the species, whereas PI exhibited mainly molecular ion signals. On the other hand, CI revealed selectivity towards different base oil groups, particularly for steranes, sulfur-containing thiophenes, and esters, yielding protonated molecular ions (M + H)⁺ for unsaturated and hydride abstracted ions (M-H⁺ ) for saturated hydrocarbons. FI, as expected, generated intact molecular ions (M^+· ) irrespective to the base oil chemical classes. It allowed elemental composition by TOFMS with a mass resolving power up to 8000 (FWHM) and a mass accuracy of 1 mDa, leading to the calculation of heteroatomic content, double bond equivalency, and carbon number of the compounds. The qualitative and quantitative results presented herein offer a unique perspective into the detailed comparison of different ionization techniques corresponding to several hydrocarbon classes.

SPME-GC×GC-TOF MS fingerprint of virally-infected cell culture: Sample preparation optimization and data processing evaluation

Untargeted metabolomics study of volatile organic compounds produced by different cell cultures is a field that has gained increasing attention over the years. Solid-phase microextraction has been the sampling technique of choice for most of the applications mainly due to its simplicity to implement. However, a careful optimization of the analytical conditions is necessary to obtain the best performances, which are highly matrix-dependent. In this work, five different solid-phase microextraction fibers were compared for the analysis of the volatiles produced by cell culture infected with the human respiratory syncytial virus. A central composite design was applied to determine the best time-temperature combination to maximize the extraction efficiency and the salting-out effect was evaluated as well. The linearity of the optimized method, along with limits of detection and quantification and repeatability was assessed. Finally, the effect of i) different normalization techniques (i.e. z-score and probabilistic quotient normalization), ii) data transformation (i.e. in logarithmic scale), and iii) different feature selection algorithms (i.e. Fisher ratio and random forest) on the capability of discriminating between infected and not-infected cell culture was evaluated.

Volatile fingerprinting of human respiratory viruses from cell culture

Volatile metabolites are currently under investigation as potential biomarkers for the detection and identification of pathogenic microorganisms, including bacteria, fungi, and viruses. Unlike bacteria and fungi, which produce distinct volatile metabolic signatures associated with innate differences in both primary and secondary metabolic processes, viruses are wholly reliant on the metabolic machinery of infected cells for replication and propagation. In the present study, the ability of volatile metabolites to discriminate between respiratory cells infected and uninfected with virus, in vitro, was investigated. Two important respiratory viruses, namely respiratory syncytial virus (RSV) and influenza A virus (IAV), were evaluated. Data were analyzed using three different machine learning algorithms (random forest (RF), linear support vector machines (linear SVM), and partial least squares-discriminant analysis (PLS-DA)), with volatile metabolites identified from a training set used to predict sample classifications in a validation set. The discriminatory performances of RF, linear SVM, and PLS-DA were comparable for the comparison of IAV-infected versus uninfected cells, with area under the receiver operating characteristic curves (AUROCs) between 0.78 and 0.82, while RF and linear SVM demonstrated superior performance in the classification of RSV-infected versus uninfected cells (AUROCs between 0.80 and 0.84) relative to PLS-DA (0.61). A subset of discriminatory features were assigned putative compound identifications, with an overabundance of hydrocarbons observed in both RSV- and IAV-infected cell cultures relative to uninfected controls. This finding is consistent with increased oxidative stress, a process associated with viral infection of respiratory cells.

Comprehensive volatile metabolic fingerprinting of bacterial and fungal pathogen groups

The identification of pathogen-specific volatile metabolic 'fingerprints' could lead to the rapid identification of disease-causing organisms either directly from ex vivo patient bio-specimens or from in vitro cultures. In the present study, we have evaluated the volatile metabolites produced by 100 clinical isolates belonging to ten distinct pathogen groups that, in aggregate, account for 90% of bloodstream infections, 90% of urinary tract infections, and 80% of infections encountered in the intensive care unit setting. Headspace volatile metabolites produced in vitro were concentrated using headspace solid-phase microextraction and analyzed via two-dimensional gas chromatography time-of-flight mass spectrometry (HS-SPME-GC×GC-TOFMS). A total of 811 volatile metabolites were detected across all samples, of which 203 were: (1) detected in 9 or 10 (of 10) isolates belonging to one or more pathogen groups, and (2) significantly more abundant in cultures relative to sterile media. Network analysis revealed a distinct metabolic fingerprint associated with each pathogen group, and analysis via Random Forest using leave-one-out cross-validation resulted in a 95% accuracy for the differentiation between groups. The present findings support the results of prior studies that have reported on the differential production of volatile metabolites across pathogenic bacteria and fungi, and provide additional insight through the inclusion of pathogen groups that have seldom been studied previously, including Acinetobacter spp., coagulase-negative Staphylococcus, and Proteus mirabilis, as well as the utilization of HS-SPME-GC×GC-TOFMS for improved sensitivity and resolution relative to traditional gas chromatography-based techniques.

Sniffing out the hypoxia volatile metabolic signature of Aspergillus fumigatus

Invasive aspergillosis (IA) is a life-threatening infectious disease caused by fungi from the genus Aspergillus, with an associated mortality as high as 90% in certain populations. IA-associated pulmonary lesions are characteristically depleted in oxygen relative to normal lung tissue, and it has been shown that the most common causal agent of IA, Aspergillus fumigatus, must respond to low-oxygen environments for pathogenesis and disease progression. Previous studies have demonstrated marked alterations to the Aspergillus fumigatus transcriptome in response to low-oxygen environments that induce a 'hypoxia response'. Consequently, we hypothesized that these transcriptomic changes would alter the volatile metabolome and generate a volatile hypoxia signature. In the present study, we analyzed the volatile molecules produced by A. fumigatus in both oxygen replete (normoxia) and depleted (hypoxia) environments via headspace solid-phase micro-extraction coupled to two-dimensional gas chromatography-time-of-flight mass spectrometry. Using the machine learning algorithm random forest, we identified 19 volatile molecules that were discriminatory between the four growth conditions assessed in this study (i.e., early hypoxia (1 h), late hypoxia (8 h), early normoxia (1 h), and late normoxia (8 h)), as well as a set of 19 that were discriminatory between late hypoxia cultures and all other growth conditions in aggregate. Nine molecules were common to both comparisons, while the remaining 20 were specific to only one of two. We assigned putative identifications to 13 molecules, of which six were most highly abundant in late hypoxia cultures. Previously acquired transcriptomic data identified putative biochemical pathways induced in hypoxia conditions that plausibly account for the production of a subset of these molecules, including 2,3-butanedione and 3-hydroxy-2-butanone. These two molecules may represent a novel hypoxia fitness pathway in A. fumigatus, and could be useful in the detection of hypoxia-associated A. fumigatus lesions that develop in established IA infections.

Comparison of the decomposition VOC profile during winter and summer in a moist, mid-latitude (Cfb) climate

The investigation of volatile organic compounds (VOCs) associated with decomposition is an emerging field in forensic taphonomy due to their importance in locating human remains using biological detectors such as insects and canines. A consistent decomposition VOC profile has not yet been elucidated due to the intrinsic impact of the environment on the decomposition process in different climatic zones. The study of decomposition VOCs has typically occurred during the warmer months to enable chemical profiling of all decomposition stages. The present study investigated the decomposition VOC profile in air during both warmer and cooler months in a moist, mid-latitude (Cfb) climate as decomposition occurs year-round in this environment. Pig carcasses (Sus scrofa domesticus L.) were placed on a soil surface to decompose naturally and their VOC profile was monitored during the winter and summer months. Corresponding control sites were also monitored to determine the natural VOC profile of the surrounding soil and vegetation. VOC samples were collected onto sorbent tubes and analyzed using comprehensive two-dimensional gas chromatography – time-of-flight mass spectrometry (GC×GC-TOFMS). The summer months were characterized by higher temperatures and solar radiation, greater rainfall accumulation, and comparable humidity when compared to the winter months. The rate of decomposition was faster and the number and abundance of VOCs was proportionally higher in summer. However, a similar trend was observed in winter and summer demonstrating a rapid increase in VOC abundance during active decay with a second increase in abundance occurring later in the decomposition process. Sulfur-containing compounds, alcohols and ketones represented the most abundant classes of compounds in both seasons, although almost all 10 compound classes identified contributed to discriminating the stages of decomposition throughout both seasons. The advantages of GC×GC-TOFMS were demonstrated for detecting and identifying trace levels of VOCs, particularly ethers, which are rarely reported as decomposition VOCs.

Enhanced characterization of the smell of death by comprehensive two-dimensional gas chromatography-time-of-flight mass spectrometry (GCxGC-TOFMS)

Soon after death, the decay process of mammalian soft tissues begins and leads to the release of cadaveric volatile compounds in the surrounding environment. The study of postmortem decomposition products is an emerging field of study in forensic science. However, a better knowledge of the smell of death and its volatile constituents may have many applications in forensic sciences. Domestic pigs are the most widely used human body analogues in forensic experiments, mainly due to ethical restrictions. Indeed, decomposition trials on human corpses are restricted in many countries worldwide. This article reports on the use of comprehensive two-dimensional gas chromatography coupled with time-of-flight mass spectrometry (GCxGC-TOFMS) for thanatochemistry applications. A total of 832 VOCs released by a decaying pig carcass in terrestrial ecosystem, i.e. a forest biotope, were identified by GCxGC-TOFMS. These postmortem compounds belong to many kinds of chemical class, mainly oxygen compounds (alcohols, acids, ketones, aldehydes, esters), sulfur and nitrogen compounds, aromatic compounds such as phenolic molecules and hydrocarbons. The use of GCxGC-TOFMS in study of postmortem volatile compounds instead of conventional GC-MS was successful.

Analysis of synthetic canine training aids by comprehensive two-dimensional gas chromatography-time of flight mass spectrometry

Cadaver dogs are trained on a variety of materials, including artificial or pseudo scents. The chemical components of commercially available pseudo scents are not known, so their accuracy as a decomposition odour mimic and their effectiveness as a canine training aid have not been evaluated. Two pseudo scents that are commercially available and used for training cadaver dogs were analysed using comprehensive two-dimensional gas chromatography-time of flight mass spectrometry (GC×GC-TOFMS). The two formulations were determined to be simplistic in their composition, compared to real cadaveric volatile organic compound (VOC) mixtures, with only a few major components. The enhanced GC×GC-TOFMS peak capacity was nevertheless useful to discriminate less intense peaks from large overloaded peaks. The availability of both dimension retention times combined with the peak finding and deconvolution algorithm, enabled the chemical characterization of the two formulations. Additionally, high resolution (HR) TOFMS was used to extract molecular formulae and confirm identities of analytes. The seven compounds identified by this work have not been reported previously as volatile products of decomposition, indicating that these pseudo scents are not to be considered as an accurate representation of cadaveric decomposition odour. Further research on the olfaction of scent detection canines and the chemical composition of their target odourants needs to be conducted to develop improved canine training aids.