A GC×GC-MS method based on solid-state modulator for non-targeted metabolomics: Comparison with traditional GC-MS method

The formidable challenge posed by the presence of extremely high amounts of compounds and large differences in concentrations in plasma significantly complicates non-targeted metabolomics analyses. In this study, a comprehensive two-dimensional gas chromatography-quadrupole mass spectrometry (GC×GC-qMS) method with a solid-state modulator (SSM) for non-targeted metabolomics in beagle plasma was first established based on a GC-MS method, and the qualitative and quantitative performance of the two platforms were compared. Identification of detected compounds was accomplished utilizing NIST database match scores, retention indices (RIs) and standards. Semi-quantification involved the calculation of peak area ratios to internal standards. Metabolite identification sheets were generated for plasma samples on both analytical platforms, featuring 22 representative metabolites chosen for validating qualitative accuracy, and for conducting comparisons of linearity, accuracy, precision, and sensitivity. The outcomes revealed a threefold increase in the number of identifiable metabolites on the GC×GC-MS platform, with lower limits of quantitation (LLOQs) reduced to 0.5-0.05 times those achieved on the GC-MS platform. Accuracy in quantification for both GC×GC-MS and GC-MS fell within the range of 85-115%, and the vast majority of intra- and inter-day precisions were within the range of 20%. These findings underscore that relative to the conventional GC-MS method, the GC×GC-MS method developed in this study, combined with SSM, exhibits enhanced qualitative capabilities, heightened sensitivity, and comparable accuracy and precision, rendering it more suitable for non-targeted metabolomics analyses.

Metabolite profiling of Piper longum L. fruit volatiles by two-dimensional gas chromatography and time-of-flight mass spectrometry: Insights into the chemical complexity

Piper longum L. (long pepper) is an economically and industrially important medicinal plant. However, the characterization of its volatiles has only been analyzed by gas chromatography-mass spectrometry (GC-MS). In the present study, precise characterization of P. longum fruit volatiles has been performed for the first time through advanced two-dimensional gas chromatography-time-of-flight spectrometry (GC×GC-TOFMS). A total of 146 constituents accounting for 93.79% were identified, of which 30 were reported for the first time. All these constituents were classified into alcohols (4.5%), alkanes (8.9%), alkenes (6.71%), esters (6.15%), ketones (0.58%), monoterpene hydrocarbons (1.64%), oxygenated monoterpenes (2.24%), sesquiterpene hydrocarbons (49.61%), oxygenated sesquiterpenes (13.03%), phenylpropanoid (0.23%), and diterpenes (0.2%). Among all the classes, sesquiterpene hydrocarbons were abundant, with germacrene-D (2.87% ± 0.01%) as the major one, followed by 8-heptadecene (2.69% ± 0.03%), β-caryophyllene (2.43% ± 0.03%), n-heptadecane (2.4% ± 0.04%), n-pentadecane (2.11% ± 0.05%), and so forth. Further, 20 constituents were observed to be coeluted and separated precisely in the two-dimensional column. The investigation provides an extensive metabolite profiling of P. longum fruit volatiles, which could be helpful to improve its therapeutic potential.

1D-Guided Differential Rescaling of a Contour Plot in Comprehensive 2D Gas Chromatography

A 1D-guided differential rescaling algorithm for a contour plot is developed based on our recently proposed comprehensive two-dimensional gas chromatography (GC × GC) system with a first-dimensional (1D) detector added. Chromatograms obtained from 1D and second-dimensional (2D) detectors are both incorporated during the data processing. As compared to the conventional contour plot methods using only 2D data, our algorithm can significantly improve precision and consistency of GC × GC results in terms of retention times, peak widths, and peak areas or volumes, regardless of modulation time selection, modulation phase shift fluctuations, and modulation duty cycle. The peak identification, quantification, and capacity can therefore be enhanced. Furthermore, the 1D-guided differential rescaling method is shown to better handle the coelution and missing peak issues often encountered in the conventional methods. Finally, the new method exhibits high versatility in 1D and 2D detector selection, which greatly broadens GC × GC utility. Our method can easily be adapted to other two-dimensional chromatography systems that have direct access to 1D chromatograms.

State-of-the-art and challenges in the analysis of renewable gases

The development of renewable and low-carbon gases for injection into the gas grid obtained by different processes such as anaerobic digestion, pyrogasification, hydrothermal gasification, and methanation, followed by upgrading steps, increases the demand for analysis and characterization in order to fully manage their integration into the gas value chain. If the analysis of the main compounds (methane, carbon dioxide, hydrogen, and carbon monoxide) is well described, the analysis of impurities in renewable gases remains more challenging due to their various natures and quantities. After a brief description of renewable and low-carbon methane production processes, the review focuses on the methods used for the analysis of the different compounds in renewable gases, from the main ones to impurities at ppbv levels. Gas chromatography (GC), coupled with different detectors, is the preferred technique, enabling the analysis and quantification of siloxanes, terpenes, oxygenates, and sulfur compounds. Recently, comprehensive two-dimensional GC has been applied to renewable gases, increasing the number of compounds detected. Non-chromatographic techniques are also reviewed. As sampling is of major importance in the search for reliable analyses, a whole section is devoted to this aspect. Among the available methods, pre-concentration on adsorbent tubes emerges as the most relevant solution.

Correlations for untargeted GC × GC-HRTOF-MS metabolomics of colorectal cancer

INTRODUCTION

Modern comprehensive instrumentations provide an unprecedented coverage of complex matrices in the form of high-dimensional, information rich data sets.

OBJECTIVES

In addition to the usual biomarker research that focuses on the detection of the studied condition, we aimed to define a proper strategy to conduct a correlation analysis on an untargeted colorectal cancer case study with a data set of 102 variables corresponding to metabolites obtained from serum samples analyzed with comprehensive two-dimensional gas chromatography coupled to high-resolution time-of-flight mass spectrometry (GC × GC-HRTOF-MS). Indeed, the strength of association existing between the metabolites contains potentially valuable information about the molecular mechanisms involved and the underlying metabolic network associated to a global perturbation, at no additional analytical effort.

METHODS

Following Anscombe's quartet, we took particular attention to four main aspects. First, the presence of non-linear relationships through the comparison of parametric and non-parametric correlation coefficients: Pearson's r, Spearman's rho, Kendall's tau and Goodman-Kruskal's gamma. Second, the visual control of the detected associations through scatterplots and their associated regressions and angles. Third, the effect and handling of atypical samples and values. Fourth, the role of the precision of the data on the attribution of the ranks through the presence of ties.

RESULTS

Kendall's tau was found the method of choice for the data set at hand. Its application highlighted 17 correlations significantly altered in the active state of colorectal cancer (CRC) in comparison to matched healthy controls (HC), from which 10 were specific to this state in comparison to the remission one (R-CRC) investigated on distinct patients. 15 metabolites involved in the correlations of interest, on the 25 unique ones obtained, were annotated (Metabolomics Standards Initiative level 2).

CONCLUSIONS

The metabolites highlighted could be used to better understand the pathology. The systematic investigation of the methodological aspects that we expose allows to implement correlation analysis to various fields and many specific cases.

Insights into the Effects of Violating Statistical Assumptions for Dimensionality Reduction for Chemical "-omics" Data with Multiple Explanatory Variables

Biological volatilome analysis is inherently complex due to the considerable number of compounds (i.e., dimensions) and differences in peak areas by orders of magnitude, between and within compounds found within datasets. Traditional volatilome analysis relies on dimensionality reduction techniques which aid in the selection of compounds that are considered relevant to respective research questions prior to further analysis. Currently, compounds of interest are identified using either supervised or unsupervised statistical methods which assume the data residuals are normally distributed and exhibit linearity. However, biological data often violate the statistical assumptions of these models related to normality and the presence of multiple explanatory variables which are innate to biological samples. In an attempt to address deviations from normality, volatilome data can be log transformed. However, whether the effects of each assessed variable are additive or multiplicative should be considered prior to transformation, as this will impact the effect of each variable on the data. If assumptions of normality and variable effects are not investigated prior to dimensionality reduction, ineffective or erroneous compound dimensionality reduction can impact downstream analyses. It is the aim of this manuscript to assess the impact of single and multivariable statistical models with and without the log transformation to volatilome dimensionality reduction prior to any supervised or unsupervised classification analysis. As a proof of concept, Shingleback lizard (Tiliqua rugosa) volatilomes were collected across their species distribution and from captivity and were assessed. Shingleback volatilomes are suspected to be influenced by multiple explanatory variables related to habitat (Bioregion), sex, parasite presence, total body volume, and captive status. This work determined that the exclusion of relevant multiple explanatory variables from analysis overestimates the effect of Bioregion and the identification of significant compounds. The log transformation increased the number of compounds that were identified as significant, as did analyses that assumed that residuals were normally distributed. Among the methods considered in this work, the most conservative form of dimensionality reduction was achieved through analyzing untransformed data using Monte Carlo tests with multiple explanatory variables.

Second-Dimension Temperature Programming System for Comprehensive Two-Dimensional Gas Chromatography. Part 2: Technical Improvements and Compatibility with Flow Modulation and Time-of-Flight Mass Spectrometry

The second-dimension (²D) temperature programming system (²DTPS) for comprehensive two-dimensional gas chromatography (GC × GC) described in Part 1 was updated and tested with the time-of-flight mass spectrometer (TOFMS) and flow modulator. Addition of a real-time clock and remote port allowed the ²DTPS to be a truly standalone system to be used with any GC × GC instrument. GC × GC reproducibility with the ²DTPS was tested with thermal and flow modulation, coupled with the TOFMS and/or FID to demonstrate compatibility with all typical GC × GC setups. An improvement in the match factor, reverse match factor, and signal-to-noise ratio was found when performing ²D temperature programming. Within-day and day-to-day reproducibility of the ²DTPS for the ¹D retention time (≤0.04 and ≤0.05%), ²D retention time (≤0.36 and ≤0.52%), and peak area (≤2.47 and ≤3.37%) were acceptable, while providing flexibility in ²D optimization and improved peak capacity.

Development and validation of a GC × GC-ToFMS method for the quantification of pesticides in environmental waters

Water is a fundamental resource for living things, which is why its control is necessary. The widespread use of pesticides for agricultural and non-agricultural purposes has resulted in the presence of their residues in surface water and groundwater resources. Their presence in water is regulated through different directives, such as the Groundwater Directive, the Drinking Water Directive, and the Water Framework Directive, modified later several times, setting a maximum concentration of 0.1 µg.L^-1 for individual pesticides and their degradation products, and 0.5 µg.L^-1 for total pesticide residues present in a sample. There are different kinds of pesticides (e.g., organophosphorus and organochlorine pesticides, triazines, chloroacetamides, triazoles, (thio)carbamates) that have diverse chemical structures. Their determination and monitoring in a single analytical procedure are possible through multiresidue methods. In this study, 53 pesticides belonging to different chemical classes and their metabolites were selected based on their local occurrence and investigated in surface water and groundwater from agricultural areas susceptible to pesticide contamination. The methodology consisted of a classical solid-phase extraction (SPE) for the purification and enrichment of the pesticides, with a subsequent analysis in multidimensional gas chromatography coupled to mass spectrometry (GC×GC-MS). The quantification method was validated according to the Eurachem Guide in terms of linearity, precision, accuracy, limit of detection, and limit of quantification. After validation, the method was applied to 34 real-world water samples, and the results were compared with those obtained by a GC-QMS routine method.

The contribution of high-resolution GC separations in plastic recycling research

One convenient strategy to reduce environmental impact and pollution involves the reuse and revalorization of waste produced by modern society. Nowadays, global plastic production has reached 367 million tons per year and because of their durable nature, their recycling is fundamental for the achievement of the circular economy objective. In closing the loop of plastics, advanced recycling, i.e., the breakdown of plastics into their building blocks and their transformation into valuable secondary raw materials, is a promising management option for post-consumer plastic waste. The most valuable product from advanced recycling is a fluid hydrocarbon stream (or pyrolysis oil) which represents the feedstock for further refinement and processing into new plastics. In this context, gas chromatography is currently playing an important role since it is being used to study the pyrolysis oils, as well as any organic contaminants, and it can be considered a high-resolution separation technique, able to provide the molecular composition of such complex samples. This information significantly helps to tailor the pyrolysis process to produce high-quality feedstocks. In addition, the detection of contaminants (i.e., heteroatom-containing compounds) is crucial to avoid catalytic deterioration and to implement and design further purification processes. The current review highlights the importance of molecular characterization of waste stream products, and particularly the pyrolysis oils obtained from waste plastics. An overview of relevant applications published recently will be provided, and the potential of comprehensive two-dimensional gas chromatography, which represents the natural evolution of gas chromatography into a higher-resolution technique, will be underlined.

Fisher ratio feature selection by manual peak area calculations on comprehensive two-dimensional gas chromatography data using standard mixtures with variable composition, storage, and interferences

Comprehensive two-dimensional gas chromatography (GC×GC) is becoming increasingly more common for non-targeted characterization of complex volatile mixtures. The information gained with higher peak capacity and sensitivity provides additional sample composition information when one-dimensional GC is not adequate. GC×GC generates complex multivariate data sets when using non-targeted analysis to discover analytes. Fisher ratio (FR) analysis is applied to discern class markers, limiting complex GC×GC profiles to the most discriminating compounds between classes. While many approaches for feature selection using FR analysis exist, FR can be calculated relatively easily directly on peak areas after any native software has performed peak detection. This study evaluated the success rates of manual FR calculation and comparison to a critical F-value for samples analyzed by GC×GC with defined concentration differences. Long-term storage of samples and other spiked interferences were also investigated to examine their impact on analyzing mixtures using this FR feature selection strategy. Success rates were generally high with mostly 90-100% success rates and some instances of percentages between 80 and 90%. There were rare cases of false positives present and a low occurrence of false negatives. When errors were made in the selection of a compound, it was typically due to chromatographic artifacts present in chromatograms and not from the FR approach itself. This work provides foundational experimental data on the use of manual FR calculations for feature selection from GC×GC data.

GC × GC-TOFMS metabolomics analysis identifies elevated levels of plasma sugars and sugar alcohols in diabetic mellitus patients with kidney failure

Two dimensional GC (GC × GC)-time-of-flight mass spectrometry (TOFMS) has been used to improve accurate metabolite identification in the chemical industry, but this method has not been applied as readily in biomedical research. Here, we evaluated and validated the performance of high resolution GC × GC-TOFMS against that of GC-TOFMS for metabolomics analysis of two different plasma matrices, from healthy controls (CON) and diabetes mellitus (DM) patients with kidney failure (DM with KF). We found GC × GC-TOFMS outperformed traditional GC-TOFMS in terms of separation performance and metabolite coverage. Several metabolites from both the CON and DM with KF matrices, such as carbohydrates and carbohydrate-conjugate metabolites, were exclusively detected using GC × GC-TOFMS. Additionally, we applied this method to characterize significant metabolites in the DM with KF group, with focused analysis of four metabolite groups: sugars, sugar alcohols, amino acids, and free fatty acids. Our plasma metabolomics results revealed 35 significant metabolites (12 unique and 23 concentration-dependent metabolites) in the DM with KF group, as compared with those in the CON and DM groups (N = 20 for each group). Interestingly, we determined 17 of the 35 (14/17 verified with reference standards) significant metabolites identified from both the analyses were metabolites from the sugar and sugar alcohol groups, with significantly higher concentrations in the DM with KF group than in the CON and DM groups. Enrichment analysis of these 14 metabolites also revealed that alterations in galactose metabolism and the polyol pathway are related to DM with KF. Overall, our application of GC × GC-TOFMS identified key metabolites in complex plasma matrices.

Simulating comprehensive two-dimensional gas chromatography mass spectrometry data with realistic run-to-run shifting to evaluate the robustness of tile-based Fisher ratio analysis

Untargeted analysis of comprehensive two-dimensional (2D) gas chromatography time-of-flight mass spectrometry (GC×GC-TOFMS) data has the potential to be hindered by run-to-run retention time shifting. To address this challenge, tile-based Fisher ratio (F-ratio) analysis (FRA) has been developed, which utilizes a supervised, untargeted approach involving a chromatographic segmentation routine termed "tiling" combined with the ANOVA F-ratio statistic to discover class-distinguishing analytes while minimizing false positives arising from shifting. The tiling algorithm is designed to account for retention shifting in both separation dimensions. Although applications of FRA have been reported, there remains a need to thoroughly evaluate the robustness of FRA for different levels of run-to-run retention shifting in order to broaden the scope of its application. To this end, a novel method of simulating GC×GC-TOFMS chromatograms with realistic run-to-run shifting is presented by random generation of low-frequency "shift functions". The dimensionless retention-time precision, <δ_r>, which is four times the standard deviation in retention time normalized to the peak width-at-base is used as a key modeling variable along with the 2D chromatographic saturation, α_e,2D, and within-class relative standard deviation in peak area, RSD_wc. We demonstrate that all three of these variables operate together to impact true positive discovery. To quantify the "success" of true positive discovery, GC×GC-TOFMS datasets for various combinations of <δ_r>, α_e,2D, and RSD_wc were simulated and then analyzed by FRA using a wide range of relative tile areas (RTA), which is a dimensionless measure of tile size. Since each hit in the FRA hit list was known a priori as either a true or false positive based on the simulation inputs, receiver operating characteristic (ROC) curves were readily constructed. Then, the area under the ROC curve (AUROC) was used as a metric for discovery "success" for various combinations of the modeling variables. Based on the results of this study, recommendations for tile size selection and experimental design are provided, and further supported by comparison to previous tile-based FRA applications. For instance, values for <δ_r>, α_e,2D, and RSD_wc obtained from a GC×GC-TOFMS dataset of yeast metabolites suggested an optimum RTA of 6.25, corresponding closely to the RTA of 4.00 employed in the study, implying the simulation results obtained here can be generalized to real datasets.

Profiling Olefins in Gasoline by Bromination Using GC×GC-TOFMS Followed by Discovery-Based Comparative Analysis

An analytical workflow for the analysis of olefins in gasoline that combines selective bromination and comprehensive two-dimensional (2D) gas chromatography time-of-flight mass spectrometry (GC×GC-TOFMS) with discovery-based analysis is reported. First, a standard mix containing n-alkanes, 1-alkenes, and aromatic species was brominated and quantified using % reacted as a metric for each compound class, defined as the difference in the total peak area between the brominated and original samples normalized to the original sample. The average % reacted (1 s.d.) values were -1.45% (2.8%) for the alkanes, 99.5% (0.4%) for the alkenes, and 6.7% (11.6%) for the aromatics, demonstrating excellent selectivity toward the alkenes with only minor aromatic bromination. The bromination chemistry was then applied to gasoline, followed by GC×GC-TOFMS analysis of the original and brominated gasoline. This GC×GC-TOFMS data set was then submitted to the supervised discovery tool tile-based F-ratio analysis (FRA), which reduced the large data set to only the chromatographic regions which distinguish between the original and brominated gasoline samples. FRA discovered 314 hits, 56 of which were determined statistically significant using combinatorial null distribution analysis (CNDA), a permutation-based significance test. Since the brominated olefins elute in an uncrowded region of the 2D chromatogram and have no signal in the original sample, their discoverability was greatly increased relative to the original olefins. By combining the information gained from brominated olefin standards and the structured patterns of the GC×GC separations, the top hits were identified as the dibromoalkane products of mono-olefins, with five C5 mono-olefins identified on a species level. The analytical workflow has broad implications for using selective reaction chemistries to facilitate supervised discovery by targeting desired compound classes.

Determining the Composition of Carbonate Solvent Systems Used in Lithium-Ion Batteries without Salt Removal

In this work, two methods were investigated for determining the composition of carbonate solvent systems used in lithium-ion (Li-ion) battery electrolytes. One method was based on comprehensive two-dimensional gas chromatography with electron ionization time-of-flight mass spectrometry (GC×GC/EI TOF MS), which often enables unknown compound identification by their electron ionization (EI) mass spectra. The other method was based on comprehensive two-dimensional gas chromatography with flame ionization detection (GC×GC/FID). Both methods were used to determine the concentrations of six different commonly used carbonates in Li-ion battery electrolytes (i.e., ethylene carbonate (EC), propylene carbonate (PC), dimethyl carbonate (DMC), diethyl carbonate (DEC), ethyl methyl carbonate (EMC), and vinylene carbonate (VC) in model compound mixtures (MCMs), single-blind samples (SBS), and a commercially obtained electrolyte solution (COES). Both methods were found to be precise (uncertainty < 5%), accurate (error < 5%), and sensitive (limit of detection <0.12 ppm for FID and <2.7 ppm for MS). Furthermore, unlike the previously reported methods, these methods do not require removing lithium hexafluorophosphate salt (LiPF6) from the sample prior to analysis. Removal of the lithium salt was avoided by diluting the electrolyte solutions prior to analysis (1000-fold dilution) and using minimal sample volumes (0.1 µL) for analysis.

Opportunities and challenges for the application of post-consumer plastic waste pyrolysis oils as steam cracker feedstocks: To decontaminate or not to decontaminate?

Thermochemical recycling of plastic waste to base chemicals via pyrolysis followed by a minimal amount of upgrading and steam cracking is expected to be the dominant chemical recycling technology in the coming decade. However, there are substantial safety and operational risks when using plastic waste pyrolysis oils instead of conventional fossil-based feedstocks. This is due to the fact that plastic waste pyrolysis oils contain a vast amount of contaminants which are the main drivers for corrosion, fouling and downstream catalyst poisoning in industrial steam cracking plants. Contaminants are therefore crucial to evaluate the steam cracking feasibility of these alternative feedstocks. Indeed, current plastic waste pyrolysis oils exceed typical feedstock specifications for numerous known contaminants, e.g. nitrogen (∼1650 vs. 100 ppm max.), oxygen (∼1250 vs. 100 ppm max.), chlorine (∼1460vs. 3 ppm max.), iron (∼33 vs. 0.001 ppm max.), sodium (∼0.8 vs. 0.125 ppm max.)and calcium (∼17vs. 0.5 ppm max.). Pyrolysis oils produced from post-consumer plastic waste can only meet the current specifications set for industrial steam cracker feedstocks if they are upgraded, with hydrogen based technologies being the most effective, in combination with an effective pre-treatment of the plastic waste such as dehalogenation. Moreover, steam crackers are reliant on a stable and predictable feedstock quality and quantity representing a challenge with plastic waste being largely influenced by consumer behavior, seasonal changes and local sorting efficiencies. Nevertheless, with standardization of sorting plants this is expected to become less problematic in the coming decade.

Determination of the Signal-To-Noise Ratio Enhancement in Comprehensive Three-Dimensional Gas Chromatography

We investigate the extent to which comprehensive three-dimensional gas chromatography (GC³) provides a signal enhancement (SE) and a signal-to-noise ratio enhancement (S/N_Rel) relative to one-dimensional (1D)-GC. Specifically, the SE is defined as the ratio of the tallest ³D peak height from the GC³ separation to the 1D peak height from the unmodulated 1D-GC separation. A model is proposed which allows the analyst to predict the theoretically attainable SE (SE_T) based upon the peak width and sampling density inputs. The model is validated via comparison of the SE_T to the experimentally measured SE (SE_M) obtained using total-transfer GC³ (100% duty cycle for both modulators) with time-of-flight mass spectrometry detection. Two experimental conditions were studied using the same GC³ column set, differing principally in the modulation period from the ¹D to ²D columns: 4 s versus 8 s. Under the first set of conditions, the average SE_M was 97 (±22), in excellent agreement with the SE_T of 97 (±18). The second set of conditions improved the average SE_M to 181 (±27), also in agreement with the average SE_T of 176 (±26). The average S/N_Rel following correction for the mass spectrum acquisition frequency was 38.8 (±11.2) and 59.0 (±27.2) for the two sets of conditions. The enhancement in S/N is largely attributed to moving the signal to a higher frequency domain where the impact of "low frequency" noise is less detrimental. The findings here provide strong evidence that GC³ separations can provide enhanced detectability relative to 1D-GC and comprehensive two-dimensional gas chromatography (GC×GC) separations.

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.