Multidimensional Gas Chromatography: Fundamental Advances and New Applications

MXene Supported Surface Plasmon Polaritons for Optical Microfiber Ammonia Sensing

The properties of surface plasmons are notoriously dependent on the supporting materials system. However, new capabilities cannot be obtained until the technique of surface plasmon enabled by advanced two-dimensional materials is well understood. Herein, we present the experimental demonstration of surface plasmon polaritons (SPPs) supported by single-layered MXene flakes (Ti3C2Tx) coating on an optical microfiber and its application as an ammonia gas sensor. Enabled by its high controllability of chemical composition, unique atomistically thin layered structure, and metallic-level conductivity, MXene is capable of supporting not only plasmon resonances across a wide range of wavelengths but also a selective sensing mechanism through frequency modulation. Theoretical modeling and optics experiments reveal that, upon adsorbing ammonia molecules, the free electron motion at the interface between the SiO2 microfiber and the MXene coating is modulated (i.e., the modulation of the SPPs under applied light), thus inducing a variation in the evanescent field. Consequently, a wavelength shift is produced, effectively realizing a selective and highly sensitive ammonia sensor with a 100 ppm detection limit. The MXene supported SPPs open a promising path for the application of advanced optical techniques toward gas and chemical analysis.

Multidimensional chromatography in environmental analysis: Comprehensive two-dimensional liquid versus gas chromatography

Analysis of complex environmental matrices poses an extreme challenge for analytical chemists due to the vast number of known and unknown compounds, with very diverse chemical and physical properties. The need for a holistic characterisation of this complexity has sparked the development of effective tools to unravel the chemical composition of such environmental samples. Multidimensional chromatographic methods, namely comprehensive two-dimensional (2D) gas and liquid chromatography (GC × GC and LC × LC, respectively), coupled to different detection systems have emerged as powerful tools with the capability to address this challenge. While GC × GC has steadily gained popularity in environmental analysis, LC × LC is surprisingly less attractive in this research field. This critical review article explores the potential reasons why LC × LC is not the dominant technique used in environmental analysis as compared to GC × GC, while simultaneously highlighting the quite unique role of LC × LC for the target and untargeted analysis of complex environmental matrices. The possible combinations of stationary phases, the important role of the interfacing valve as the heart of an LC × LC assembly, the existing optimization strategies for improving the separation power in the 2D chromatographic space, and the need for user-friendly mathematical tools for multidimensional data handling are also discussed. Finally, a set of practical measures are suggested to increase the use and secure the success of LC × LC in environmental analysis.

Comprehensive two-dimensional gas chromatography under low-pressure conditions

The analysis of thermally labile and high-boiling point compounds by gas chromatography (GC) can be a challenge. One technique to overcome these challenges is low-pressure GC, which uses the vacuum produced from the mass spectrometer and wide-bore columns to elute compounds at significantly lower temperatures. While GC-MS is a powerful technique, comprehensive two-dimensional gas chromatography (GC × GC), allows for resolution of compounds that would typically coelute using GC. In this study, a pesticide standard mixture (8270 MegaMix Standard) was analyzed using a conventional GC × GC-TOFMS configuration (0.25 mm inner diameter (i.d.) to a 0.18 mm i.d. column) and low-pressure GC × GC-TOFMS configuration (0.53 mm i.d. to a 0.53 mm i.d. column). Elution temperatures, sensitivity, and peak capacity were investigated for both configurations. Compounds eluted an average of 30 °C less on the low-pressure GC × GC-TOFMS configuration compared to the conventional GC × GC-TOFMS configuration. Moreover, the compounds were separated in ∼13 min on the low-pressure GC × GC-TOFMS as opposed to 33 min for conventional GC × GC-TOFMS. However, due to the wide-bore columns and faster runtimes the low-pressure GC × GC-TOFMS had a lower, β corrected 2D peak capacity, nc,β,2D, of 1260 while the conventional GC × GC-TOFMS was 3588. Interestingly, both configurations yielded a similar peak capacity production of 93 peaks/min and 107 peaks/min for low-pressure and conventional GC × GC-TOFMS, respectively. A "real world" sample of diesel fuel was tested on the low-pressure and conventional GC × GC-TOFMS configurations and similar results were obtained compared to the pesticide standard mix except the peak capacity production of the low-pressure GC × GC-TOFMS configuration was higher than that of the conventional GC × GC-TOFMS method.

Volatile Composition of Fortification Grape Spirit and Port Wine: Where Do We Stand?

Port wine's prominence worldwide is unequivocal and the grape spirit, which comprises roughly one fifth of the total volume of this fortified wine, is also a contributor to the recognized quality of this beverage. Nonetheless, information about the influence of the grape spirit on the final aroma of Port wine, as well as its volatile composition, is extremely limited. Moreover, the aroma characteristics of Port wines are modulated mainly by their volatile profiles. Hence, this review presents a detailed overview of the volatile composition of the fortification spirit and Port wine, along with the methodologies employed for their characterization. Moreover, it gives a general overview of the Douro Demarcated Region (Portugal) and the relevance of fortification spirit to the production of Port wine. As far as we know, this review contains the most extensive database on the volatile composition of grape spirit and Port wine, corresponding to 23 and 208 compounds, respectively. To conclude, the global outlook and future challenges are addressed, with the position of the analytical coverage of the chemical data on volatile components discussed as crucial for the innovation centered on consumer preferences.

Comprehensive Two-Dimensional Gas Chromatography as a Bioanalytical Platform for Drug Discovery and Analysis

Over the last decades, comprehensive two-dimensional gas chromatography (GC×GC) has emerged as a significant separation tool for high-resolution analysis of disease-associated metabolites and pharmaceutically relevant molecules. This review highlights recent advances of GC×GC with different detection modalities for drug discovery and analysis, which ideally improve the screening and identification of disease biomarkers, as well as monitoring of therapeutic responses to treatment in complex biological matrixes. Selected recent GC×GC applications that focus on such biomarkers and metabolite profiling of the effects of drug administration are covered. In particular, the technical overview of recent GC×GC implementation with hyphenation to the key mass spectrometry (MS) technologies that provide the benefit of enhanced separation dimension analysis with MS domain differentiation is discussed. We conclude by highlighting the challenges in GC×GC for drug discovery and development with perspectives on future trends.

Multidimensional Gas Chromatography: Benefits and Considerations for Current and Prospective Users

Two-dimensional gas chromatography (GC×GC) offers improved separation power for complex samples containing hundreds to thousands of analytes. However, several considerations must be made to determine whether multidimensional gas chromatography (MDGC) is the logical instrument choice to answer a particular scientific question, including, but not limited to, whether the analysis is targeted or non-targeted, the number of analytes of interest, and the presence of interferences that are coeluted, as well as any potential regulatory or industrial constraints. Currently, MDGC remains daunting for many users because of data complexity and the limited tools commercially available, which are critical for improving the accessibility of MDGC. Herein, we discuss considerations that may assist analysts, laboratory managers, regulatory agents, instrument and software vendors, and those interested in understanding the applicability of 2D-GC for the scientific question being investigated.

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.

A procedure to detect and identify specific chemicals of potential inhalation toxicity concern in aerosols

OBJECTIVE

Understanding the potential inhalation toxicity of poorly characterized aerosols is challenging both because aerosols may contain numerous chemicals and because it is difficult to predict which chemicals may present significant inhalation toxicity concerns at the observed levels. We have developed a novel systematic procedure to address these challenges through non-targeted chemical analysis by two-dimensional gas chromatography-time-of-flight mass spectrometry (GC × GC-TOFMS) and assessment of the results using publicly available toxicity data to prioritize the tentatively identified detected chemicals according to potential inhalation toxicity.

MATERIALS AND METHODS

The procedure involves non-targeted chemical analysis of aerosol samples utilizing GC × GC-TOFMS, which is selected because it is an effective technique for detecting chemicals in complex samples and assigning tentative identities according to the mass spectra. For data evaluation, existing toxicity data (e.g. from the U.S. Environmental Protection Agency CompTox Chemicals Dashboard) are used to calculate multiple toxicity metrics that can be compared among the tentatively identified chemicals. These metrics include hazard quotient, incremental lifetime cancer risk, and metrics analogous to hazard quotient that we designated as exposure-(toxicology endpoint) ratios.

RESULTS AND DISCUSSION

We demonstrated the utility of our procedure by detecting, identifying, and prioritizing specific chemicals of potential inhalation toxicity concern in the mainstream smoke generated from the machine-smoking of marijuana blunts.

CONCLUSION

By designing a systematic approach for detecting and identifying numerous chemicals in complex aerosol samples and prioritizing the chemicals in relation to different inhalation toxicology endpoints, we have developed an effective approach to elucidate the potential inhalation toxicity of aerosols.

Aroma Clouds of Foods: A Step Forward to Unveil Food Aroma Complexity Using GC × GC

The human senses shape the life in several aspects, namely well-being, socialization, health status, and diet, among others. However, only recently, the understanding of this highly sophisticated sensory neuronal pathway has gained new advances. Also, it is known that each olfactory receptor cell expresses only one type of odorant receptor, and each receptor can detect a limited number of odorant substances. Odorant substances are typically volatile or semi-volatile in nature, exhibit low relative molecular weight, and represent a wide variety of chemical families. These molecules may be released from foods, constituting clouds surrounding them, and are responsible for their aroma properties. A single natural aroma may contain a huge number of volatile components, and some of them are present in trace amounts, which make their study especially difficult. Understanding the components of food aromas has become more important than ever with the transformation of food systems and the increased innovation in the food industry. Two-dimensional gas chromatography and time-of-flight mass spectrometry (GC × GC-ToFMS) seems to be a powerful technique for the analytical coverage of the food aromas. Thus, the main purpose of this review is to critically discuss the potential of the GC × GC–based methodologies, combined with a headspace solvent-free microextraction technique, in tandem with data processing and data analysis, as a useful tool to the analysis of the chemical aroma clouds of foods. Due to the broad and complex nature of the aroma chemistry subject, some concepts and challenges related to the characterization of volatile molecules and the perception of aromas will be presented in advance. All topics covered in this review will be elucidated, as much as possible, with examples reported in recent publications, to make the interpretation of the fascinating world of food aroma chemistry more attractive and perceptive.

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.

Latest Trends on the Future of Three-Dimensional Separations in Chromatography

Separation and characterization of complex mixtures are of crucial importance in many fields, where extremely high separation power is required. Three-dimensional separation techniques can offer a path toward achieving high peak capacities. In this Review, online three-dimensional separation systems are discussed, including three-dimensional gas chromatography, and hyphenated combinations of two-dimensional gas chromatography with liquid chromatography or supercritical-fluid chromatography. Online comprehensive two-dimensional liquid chromatography provides detailed information on complex samples and the need for higher peak capacities is pushing researchers toward online three-dimensional liquid chromatography. In this review, an overview of the various combinations are provided and we discuss and compare their potential performance, advantages, perspectives, and results obtained during the most recent 10-15 years. Finally, the Review will discuss a novel approach of spatial three-dimensional liquid separation to increase peak capacity.

Determination of vacuum ultraviolet detector response factors by hyphenation with two-dimensional comprehensive gas chromatography with flame ionization detection

Two-dimensional comprehensive gas chromatography is an established technique, employed for the characterization of complex samples. Broadband vacuum ultraviolet absorption spectroscopy detection has recently attracted a lot of attention as it is a universal detection technique characterized by good selectivity but also ease of use and amenability to coupling with two-dimensional comprehensive gas chromatography. Vacuum ultraviolet spectroscopy is particularly interesting due to the possibility of performing spectral decomposition for species that coelute in gas chromatography analysis. This detector has quantitative capabilities, however not all species absorb vacuum ultraviolet radiation the same. Unfortunately, vacuum ultraviolet relative response factors for compounds are not always available. Methods to rapidly measure vacuum ultraviolet relative response factors and generate a large database that would allow calibration free quantitative analysis of complex mixtures are therefore of great interest. In this work, a universal methodology that permits rapid measurement of vacuum ultraviolet relative response factors is reported. It involves flow modulated two-dimensional comprehensive gas chromatography with dual vacuum ultraviolet and flame ionization detection. In this set-up, flame ionization detection is employed as a quantitative reference allowing to scale vacuum ultraviolet responses of investigated compounds. This approach was validated by flow measurements and by comparing relative response factors obtained for model compounds with literature data.

Strategies for the identification and sensory evaluation of volatile constituents in wine

Wine aroma, which stems from complex perceptual and cognitive processes, is initially driven by a multitude of naturally occurring volatile constituents. Its interpretation depends on the characterization of relevant volatile constituents. With large numbers of volatile constituents already identified, the search for unknown volatiles in wine has become increasingly challenging. However, the opportunities to discover unknown volatile compounds contributing to the wine volatilome are still of great interest, as demonstrated by the recent identification of highly odorous trace (µg/L) to ultra-trace (ng/L) volatile compounds in wine. This review provides an overview of both existing strategies and future directions on identifying unknown volatile constituents in wine. Chemical identification, including sample extraction, fractionation, gas chromatography, olfactometry, and mass spectrometry, is comprehensively covered. In addition, this review also focuses on aspects related to sensory-guided wine selection, authentic reference standards, artifacts and interferences, and the evaluation of the sensory significance of discovered wine volatiles. Powerful key volatile odorants present at ultra-trace levels, for which these analytical approaches have been successfully applied, are discussed. Research areas where novel wine volatiles are likely to be identified are pointed out. The importance of perceptual interaction phenomena is emphasized. Finally, future avenues for the exploration of yet unknown wine volatiles by coupling analytical approaches and sensory evaluation are suggested.

Automated Screening and Filtering Scripts for GC×GC-TOFMS Metabolomics Data

Comprehensive two-dimensional gas chromatography mass spectrometry (GC×GC-MS) is a powerful tool for the analysis of complex mixtures, and it is ideally suited to discovery studies where the entire sample is potentially of interest. Unfortunately, when unit mass resolution mass spectrometers are used, many detected compounds have spectra that do not match well with libraries. This could be due to the compound not being in the library, or the compound having a weak/nonexistent molecular ion cluster. While high-speed, high-resolution mass spectrometers, or ion sources with softer ionization than 70 eV electron impact (EI) may help with some of this, many GC×GC systems presently in use employ low-resolution mass spectrometers and 70 eV EI ionization. Scripting tools that apply filters to GC×GC-TOFMS data based on logical operations applied to spectral and/or retention data have been used previously for environmental and petroleum samples. This approach rapidly filters GC×GC-TOFMS peak tables (or raw data) and is available in software from multiple vendors. In this work, we present a series of scripts that have been developed to rapidly classify major groups of compounds that are of relevance to metabolomics studies including: fatty acid methyl esters, free fatty acids, aldehydes, alcohols, ketones, amino acids, and carbohydrates.

Unique ion filter-A data reduction tool for chemometric analysis of raw comprehensive two-dimensional gas chromatography-mass spectrometry data

Comprehensive gas chromatography with time of flight mass spectrometry is a powerful tool in the analysis of complex samples. Chemometric analysis of raw chromatographic data is more useful in one- and two-dimensional separations relative to peak tables. The data volume from such experiments generally necessitates the use of data reduction tools. Such tools often sacrifice some of the multivariate information in the mass to charge ratio dimension. The unique ion filter reduces the over-redundancy in two-dimensional gas chromatography-mass spectrometry data by limiting the data to a few unique/pseudo-unique ions, sub-peaks/slices in the first dimension, and spectra in the second dimension. We explore the performance of this algorithm through careful inspection of two-dimensional gas chromatography-mass spectrometry data before and after application of the filter. A reduction (99%) in the number of variables in a two-dimensional gas chromatography-mass spectrometry chromatogram passed on to subsequent analysis was observed. Feature selection times for model optimization reduced from 229 (±13) to 6.8 (±0.5) min when the filter was applied. An estimate of two unique/pseudo-unique ions, one sub-peak in the first dimension and five spectra in the second dimension were considered to provide a true representation of each chromatogram and provided enough information to achieve 100% model prediction accuracy.

Investigation of the limit of discovery using tile-based Fisher ratio analysis with comprehensive two-dimensional gas chromatography time-of-flight mass spectrometry

Comprehensive two-dimensional gas chromatography coupled with time-of-flight mass spectrometry (GC×GC-TOFMS) is followed by tile-based Fisher ratio (F-ratio) analysis to investigate the "limit of discovery" for low concentration levels of sulfur-containing compounds in JP8 jet fuel. A mixture of 14 sulfur-containing compounds was spiked at 30 ppm, 15 ppm, 3 ppm and 1.5 ppm into the neat fuel prior to GC×GC-TOFMS analysis with a "reversed" column format (aka polar first dimension (¹D) and non-polar second dimension (²D) column). Prior standard implementation of tile-based F-ratio analysis utilized an average F-ratio requiring a minimum of 3 mass channels (m/z) with the highest F-ratios. Herein, we explore the notion that use of the top F-ratio m/z for hitlist ranking is superior to the standard implementation for analytes near their limit-of-quantitation (LOQ), defined as an analyte concentration that produces a signal equal to ten times the standard deviation of the baseline noise (10σ_n). Hitlist ranking comparisons revealed that using only the top F-ratio m/z resulted in impressive improvements in discoverability for the low concentration comparisons. Specifically, for the 3 ppm versus neat hitlist, 1,4-oxathiane (LOQ = 2.5 ppm) improved from hit 114 via standard F-ratio analysis, to hit 25. For the 1.5 ppm versus neat hitlist, 2-propylthiophene (LOQ = 0.64 ppm) improved from hit 59 to 17, benzo[b]thiophene (LOQ = 1.1 ppm) from hit 98 to 28, and 2,5-dimethylthiophene (LOQ = 1.3 ppm) from hit 262 to 39. Additional hitlist ranking comparisons revealed the importance of proper tile size selection, as analyte discoverability deteriorated upon using either an inappropriately too small or too large of a tile.

Application of Chemometrics for Coal Pyrolysis Products by Online py-GC×GC-MS

Investigations on the molecular composition of coal pyrolysis products can help us to improve nonfuel utilization of coal. Meanwhile, the molecular composition of coal pyrolysis products is also influenced by the characteristics and depositional environment of coal. However, due to the extremely complex nature of coal, direct investigation of the molecular composition of coal pyrolysis products is still a challenge. In the present work, the data of the molecular composition of bituminous coal pyrolysis products are obtained by online pyrolysis coupled to comprehensive two-dimensional gas chromatography and mass spectrometry (online py-GC×GC-MS) and are divided into nine molecular groups depending on the aromaticity of the pyrolysis products and separating power of the GC×GC-MS. Chemometric tools, hierarchical cluster analysis, and principal component analysis are employed to reveal the correlations among the molecular composition of coal pyrolysis products and coal characteristics. The results show that the nine molecular groups of bituminous coal pyrolysis products can be divided into two clusters, the "aromatic group" and the "aliphatic group", and that the eight coals are divided into three clusters, all of which can be interpreted by the depositional environments and δ¹³C_VPDB values of coals. Moreover, a simple and empirical equation for estimation of coal tar from hydropyrolysis can be obtained depending on the chemometric results of the molecular composition of the coal pyrolysis products. By application of chemometrics, the molecular composition of coal pyrolysis products can provide preference to industrial utilization of coal.

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

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

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.

Statistical inference of mass channel purity from Fisher ratio analysis using comprehensive two-dimensional gas chromatography with time of flight mass spectrometry data

Tile-based Fisher ratio (F-ratio) analysis has recently been developed and validated for discovery-based studies of highly complex data collected using comprehensive two-dimensional gas chromatography coupled with time-of-flight mass spectrometry (GC×GC-TOFMS). In previous studies, interpretation and utilization of F-ratio hit lists has relied upon manual decomposition and quantification performed by chemometric methods such as parallel factor analysis (PARAFAC), or via manual translation of the F-ratio hit list information to peak table quantitative information provided by the instrument software (ChromaTOF). Both of these quantification approaches are bottlenecks in the overall workflow. In order to address this issue, a more automatable approach to provide accurate relative quantification for F-ratio analyses was investigated, based upon the mass spectral selectivity provided via the F-ratio spectral output. Diesel fuel spiked with 15 analytes at four concentration levels (80, 40, 20, and 10 ppm) produced three sets of two class comparisons that were submitted to tile-based F-ratio analysis to obtain three hit lists, with an F-ratio spectrum for each hit. A novel algorithm which calculates the signal ratio (S-ratio) between two classes (eg., 80 ppm versus 40 ppm) was applied to all mass channels (m/z) in the F-ratio spectrum for each hit. A lack of fit (LOF) metric was utilized as a measure of peak purity and combined with F-ratio and p-values to study the relationship of each of these metrics with m/z purity. Application of a LOF threshold coupled with a p-value threshold yielded a subset of the most pure m/z for each of the 15 spiked analytes, evident by the low deviations (< 5%) in S-ratio relative to the true concentration ratio. A key outcome of this study was to demonstrate the isolation of pure m/z without the need for higher level signal decomposition algorithms.

Analysis of Organic Sulphur Compounds in Coal Tar by Using Comprehensive Two-Dimensional Gas Chromatography-High Resolution Time-of-Flight Mass Spectrometry

Coal tar is a complex mixture of organic compounds obtained from the thermal treatment of coal; it contains several different chemical classes of compounds, such as polycyclic aromatic hydrocarbons, phenols and different heterocyclic compounds including sulphur derivatives. In the present research, a target analysis was carried out for the characterisation of fourteen different classes of organic sulphur compounds in coal tar by using cryogenically-modulated (CM) comprehensive two-dimensional gas chromatography-high resolution time-of-flight mass spectrometry (GC×GC-HR ToFMS) with the support of target analyte finding, a specific software function. Furthermore, absolute quantification data were obtained by using eight pure standard sulphur compounds, and 1-fluoronaphthalene as internal standard. Several figures-of-merit of the proposed method were measured (linearity, intra-day precision, limits of detection and quantification). Finally, the overall analytical performance of CM GC×GC-HR ToFMS was evaluated, in relation to MS similarities, mass accuracies, second-dimension peak widths, peak capacity and tailing factors. The approach proved itself as being a powerful analytical platform, benefiting from the high sensitivity, selectivity and resolving power, of both the GC and MS sides.

Metal-Organic Framework Stationary Phases for One- and Two-Dimensional Micro-Gas Chromatographic Separations of Light Alkanes and Polar Toxic Industrial Chemicals

Despite promising advances with metal-organic frameworks (MOFs) as stationary phases for chromatography, the application of MOFs for one- and two-dimensional micro-gas chromatography (μGC and μGC × μGC) applications has yet to be shown. We demonstrate for the first time, μGC columns coated with two different MOFs, HKUST-1 and ZIF-8, for the rapid separation of high volatility light alkane hydrocarbons (natural gas) and determined the partition coefficients for toxic industrial chemicals, using μGC and μGC × μGC systems. Complete separation of natural gas components, methane through pentane, was completed within 1 min, with sufficient resolution to discriminate n-butane from i-butane. Layer-by-layer controlled deposition cycles of the MOFs were accomplished to establish the optimal film thickness, which was validated using GC (sorption thermodynamics), quartz-crystal microbalance gravimetric analysis and scanning electron microscopy. Complete surface coverage was not observed until after ~17 deposition cycles. Propane retention factors with HKUST-1-coated μGC and a state-of-the-art polar, porous-layer open-tubular (PLOT) stationary phase were approximately the same at ~7.5. However, with polar methanol, retention factors with these two stationary phases were 748 and 59, respectively, yielding methanol-to-propane selectivity factors of ~100 and ~8, respectively, a 13-fold increase in polarity with HKUST-1. These studies advance the applications of MOFs as μGC stationary phase.

Metabolic Profiling of Varronia curassavica Jacq. Terpenoids by Flow Modulated Two-Dimensional Gas Chromatography Coupled to Mass Spectrometry

In this study, a metabolomic approach was used to investigate the effect of seasonality on the chemical composition and yield of anti-inflammatory active principle, α-humulene, in the essential oil of three genotypes of Varronia curassavica Jacq. (Syn. Cordia verbenaceae). The essential oils were extracted by hydrodistillation and analyzed by comprehensive two-dimensional gas chromatography coupled to mass spectrometry (GC×GC-MS). The GC×GC approach a three-fold improvement in qualitative analysis (48 compounds were identified by GC-MS versus 135 by GC×GC-MS). The improved resolving power of GC×GC resolved important coelutions and enabled the detection of unusual substances in V. curassavica essential oil. The chromatographic data was analyzed by using peak table-based chemometrics, namely, principal component analysis (PCA) and hierarchical cluster analysis (HCA). The metabolic study showed that seasonality has a significant effect on the chemical composition. The α-humulene content was affected by genotype and season. Spring and summer were the best harvest seasons for the yield of the active ingredient, found in higher concentrations in the VC2 genotype. The proposed metabolomic workflow was successfully applied to terpene analysis found in V. curassavica essential oil, and such results have broadened our understanding of the influence of seasonal factors on the specialized metabolism of the species.

A high-repetition-rate, fast temperature-programmed gas chromatograph and its online coupling to a supercritical fluid chromatograph (SFC × GC)

We present a fast gas chromatographic system that can be used as a second dimension in comprehensive two-dimensional (supercritical fluid × gas) chromatography (SFC × GC). The temperature of the short (1 m long) capillary column is controlled by a resistively heated coaxial stainless-steel tube. The electrical resistance and, therefore, temperature of the stainless-steel tube are measured by continuous monitoring of the current/voltage ratio. Highly repeatable heating rates of up to 2100 °C min^-1 (35 °C s^-1) are obtained, which should be high enough for the most demanding fast chromatograms. To reduce the cooling time between temperature programs, the column is cooled by injecting evaporating carbon dioxide into the space between the coaxial heater and the column. This gives cooling rates of 5100 °C min^-1 (85 °C s^-1), which allows quick succession of temperature programs. More repeatable heating profiles with stable GC retention times together with faster cooling are significant improvements on previous SFC × GC systems. Cycle times of four gas chromatograms per minute could readily be achieved, which allows efficient coupling to high-resolution stop-flow SFC in the first dimension. We demonstrate the fast chromatograph by separating fatty acid methyl esters, yielding information that would be useful in the food and biodiesel industries.

Gas chromatography vacuum ultraviolet spectroscopy: A review

Accelerated technological progress and increased complexity of interrogated matrices imposes a demand for fast, powerful, and resolutive analysis techniques. Gas chromatography has been for a long time a 'go-to' technique for the analysis of mixtures of volatile and semi-volatile compounds. Coupling of the several dimensions of gas chromatography separation has allowed to access a realm of improved separations in the terms of increased separation power and detection sensitivity. Especially comprehensive separations offer an insight into detailed sample composition for complex samples. Combining these advanced separation techniques with an informative detection system such as vacuum ultraviolet spectroscopy is therefore of great interest. Almost all molecules absorb the vacuum ultraviolet radiation and have distinct spectral features with compound classes exhibiting spectral signature similarities. Spectral information can be 'filtered' to extract the response in the most informative spectral ranges. Developed algorithms allow spectral mixture estimation of coeluting species. Vacuum ultraviolet detector follows Beer-Lambert law, with the possibility of calibrationless quantitation. The purpose of this article is to provide an overview of the features and specificities of gas chromatography-vacuum ultraviolet spectroscopy coupling which has gained interest since the recent introduction of a commercial vacuum ultraviolet detector. Potentials and limitations, relevant theoretical considerations, recent advances and applications are explored.

Rapid breath analysis for acute respiratory distress syndrome diagnostics using a portable two-dimensional gas chromatography device

Acute respiratory distress syndrome (ARDS) is the most severe form of acute lung injury, responsible for high mortality and long-term morbidity. As a dynamic syndrome with multiple etiologies, its timely diagnosis is difficult as is tracking the course of the syndrome. Therefore, there is a significant need for early, rapid detection and diagnosis as well as clinical trajectory monitoring of ARDS. Here, we report our work on using human breath to differentiate ARDS and non-ARDS causes of respiratory failure. A fully automated portable 2-dimensional gas chromatography device with high peak capacity (> 200 at the resolution of 1), high sensitivity (sub-ppb), and rapid analysis capability (~ 30 min) was designed and made in-house for on-site analysis of patients' breath. A total of 85 breath samples from 48 ARDS patients and controls were collected. Ninety-seven elution peaks were separated and detected in 13 min. An algorithm based on machine learning, principal component analysis (PCA), and linear discriminant analysis (LDA) was developed. As compared to the adjudications done by physicians based on the Berlin criteria, our device and algorithm achieved an overall accuracy of 87.1% with 94.1% positive predictive value and 82.4% negative predictive value. The high overall accuracy and high positive predicative value suggest that the breath analysis method can accurately diagnose ARDS. The ability to continuously and non-invasively monitor exhaled breath for early diagnosis, disease trajectory tracking, and outcome prediction monitoring of ARDS may have a significant impact on changing practice and improving patient outcomes. Graphical abstract.

Characterization of Key Aroma Compounds in a Commercial Rum and an Australian Red Wine by Means of a New Sensomics-Based Expert System (SEBES)-An Approach To Use Artificial Intelligence in Determining Food Odor Codes

Although to date more than 10 000 volatile compounds have been characterized in foods, a literature survey has previously shown that only 226 aroma compounds, assigned as key food odorants (KFOs), have been identified to actively contribute to the overall aromas of about 200 foods, such as beverages, meat products, cheeses, or baked goods. Currently, a multistep analytical procedure involving the human olfactory system, assigned as Sensomics, represents a reference approach to identify and quantitate key odorants, as well as to define their sensory impact in the overall food aroma profile by so-called aroma recombinates. Despite its proven effectiveness, the Sensomics approach is time-consuming because repeated sensory analyses, for example, by GC/olfactometry, are essential to assess the odor quality and potency of each single constituent in a given food distillate. Therefore, the aim of the present study was to develop a fast, but Sensomics-based expert system (SEBES) that is able to reliably predict the key aroma compounds of a given food in a limited number of runs without using the human olfactory system. First, a successful method for the quantitation of nearly 100 (out of the 226 known KFOs) components was developed in combination with a software allowing the direct use of the identification and quantitation data for the calculation of odor activity values (OAV; ratio of concentration to odor threshold). Using a rum and a wine as examples, the quantitative results obtained by the new SEBES method were compared to data obtained by applying an aroma extract dilution analysis and stable isotope dilution assays required in the classical Sensomics approach. A good agreement of the results was found with differences below 20% for most of the compounds considered. By implementing the GC × GC data analysis software with the in-house odor threshold database, odor activity values (ratio of concentration to odor threshold) were directly displayed in the software pane. The OAVs calculated by the software were in very good agreement with data manually calculated on the basis of the data obtained by SIDA. Thus, it was successfully shown that it is possible to characterize key food odorants with one single analytical platform and without using the human olfactory system, that is, by "artificial intelligence smelling".