Standardized test mixture for the characterization of comprehensive two-dimensional gas chromatography columns: the Phillips mix

Serum metabolic profiling in pregnant Holstein cows 3 weeks prior to parturition using two-dimensional gas chromatography time-of-flight mass spectrometry

This preliminary study explored potential serum biomarkers for predicting the onset of milk fever (MF), a bovine parturient disease with hypocalcemia. We conducted two-dimensional gas chromatography mass spectrometry-based metabolomics in 8 and 17 pregnant Holstein cows that did and did not develop MF 3 weeks later, respectively. In principal component analysis (PCA) applied to a dataset containing 1,498 metabolites, serum metabolites exhibited highly similar chemical profiles between cows with and without MF. PCA with a limited dataset of metabolites containing fatty acids, which had significantly different values between the groups and/or correlation coefficients of >0.5 for the serum calcium concentration, distinguished the two groups. These suggest the possibility of developing serum biomarkers for predicting bovine MF.

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.

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.

Comprehensive Two-Dimensional Gas Chromatography Mass Spectrometry-Based Metabolomics

Compared to one-dimensional gas chromatography with mass spectrometry (GC-MS), GC × GC-MS provides significantly increased peak capacity, resolution, and sensitivity for analysis of complex biological samples. In the last decade, GC × GC-MS has been increasingly applied to the discovery of metabolite biomarkers and elucidation of metabolic mechanisms in human diseases. The recent development of coupling GC × GC with a high-resolution mass spectrometer further accelerates these metabolomic applications. In this chapter, we will briefly review the instrumentation, sample preparation, data analysis, and applications of GC × GC-MS-based metabolomic analysis.

Analysis of volatile organic compounds in indoor environments using thermal desorption with comprehensive two-dimensional gas chromatography and high-resolution time-of-flight mass spectrometry

Building-related health effects are frequently observed. Several factors have been listed as possible causes including temperature, humidity, light conditions, presence of particulate matter, and microorganisms or volatile organic compounds. To be able to link exposure to specific volatile organic compounds to building-related health effects, powerful and comprehensive analytical methods are required. For this purpose, we developed an active air sampling method that utilizes dual-bed tubes loaded with TENAX-TA and Carboxen-1000 adsorbents to sample two parallel air samples of 4 L each. For the comprehensive volatile organic compounds analysis, an automated thermal desorption comprehensive two-dimensional gas chromatography high-resolution time-of-flight mass spectrometry method was developed and used. It allowed targeted analysis of approximately 90 known volatile organic compounds with relative standard deviations below 25% for the vast majority of target volatile organic compounds. It also allowed semiquantification (no matching standards) of numerous nontarget air contaminants using the same data set. The nontarget analysis workflow included peak finding, background elimination, feature alignment, detection frequency filtering, and tentative identification. Application of the workflow to air samples from 68 indoor environments at a large hospital complex resulted in a comprehensive volatile organic compound characterization, including 178 single compounds and 13 hydrocarbon groups.

Retention time prediction of hydrocarbons in cryogenically modulated comprehensive two-dimensional gas chromatography: A method development and translation application

Thermodynamic modeling of GC × GC separations provides a tool for rapid method evaluation and optimization. Separations of 95 hydrocarbons on two cryogenically modulated GC × GC systems (atmospheric outlet and vacuum outlet) are modeled, displaying average second dimension retention time modeling absolute errors of 0.17 s and 0.12 s respectively, and generating modeled chromatograms which sufficiently represent experimental data. A web-based GC × GC modeling routine is presented which allows users to model separations, currently focused on hydrocarbons, with full control over all system parameters. The method translation capabilities of the application are further demonstrated by replicating Piotrowski et al.'s GC × GC-HRT temporal distribution plots of hydraulic fracturing flowback fluid hydrocarbons [28].

Multimodal chemometric approach for the analysis of human exhaled breath in lung cancer patients by TD-GC × GC-TOFMS

Lung cancer is the deadliest cancer in developed countries. To reduce its mortality rate, it is important to enhance our capability to detect it at earlier stages by developing early diagnostic methods. In that context, the analysis of exhaled breath is an interesting approach because of the simplicity of the medical act and its non-invasiveness. Thermal desorption comprehensive two-dimensional gas chromatography time of flight mass spectrometry (TD-GC × GC-TOFMS) has been used to characterize and compare the volatile content of human breath of lung cancer patients and healthy volunteers. On the sampling side, the contaminations induced by the bags membrane and further environmental migration of VOCs during and after the sampling have also been investigated. Over a realistic period of 6 h, the concentration of contaminants inside the bag can increase from 2 to 3 folds based on simulated breath samples. On the data processing side, Fisher ratio (FR) and random forest (RF) approaches were applied and compared in regards to their ability to reduce the data dimensionality and to extract the significant information. Both approaches allow to efficiently smooth the background signal and extract significant features (27 for FR and 17 for RF). Principal component analysis (PCA) was used to evaluate the clustering capacity of the different models. For both approaches, a separation along PC-1 was obtained with a variance score around 35%. The combined model provides a partial separation with a PC-1 score of 52%. This proof-of-concept study further confirms the potential of breath analysis for cancer detection but also underlines the importance of quality control over the full analytical procedure, including the processing of the data.

Retention-time prediction in comprehensive two-dimensional gas chromatography to aid identification of unknown contaminants

Comprehensive two-dimensional (2D) gas chromatography (GC×GC) coupled to mass spectrometry (MS, GC×GC-MS), which enhances selectivity compared to GC-MS analysis, can be used for non-directed analysis (non-target screening) of environmental samples. Additional tools that aid in identifying unknown compounds are needed to handle the large amount of data generated. These tools include retention indices for characterizing relative retention of compounds and prediction of such. In this study, two quantitative structure–retention relationship (QSRR) approaches for prediction of retention times (¹t_R and ²t_R) and indices (linear retention indices (LRIs) and a new polyethylene glycol–based retention index (PEG-²I)) in GC × GC were explored, and their predictive power compared. In the first method, molecular descriptors combined with partial least squares (PLS) analysis were used to predict times and indices. In the second method, the commercial software package ChromGenius (ACD/Labs), based on a “federation of local models,” was employed. Overall, the PLS approach exhibited better accuracy than the ChromGenius approach. Although average errors for the LRI prediction via ChromGenius were slightly lower, PLS was superior in all other cases. The average deviations between the predicted and the experimental value were 5% and 3% for the ¹t_R and LRI, and 5% and 12% for the ²t_R and PEG-²I, respectively. These results are comparable to or better than those reported in previous studies. Finally, the developed model was successfully applied to an independent dataset and led to the discovery of 12 wrongly assigned compounds. The results of the present work represent the first-ever prediction of the PEG-²I.

Graphical abstract

Molecular Characterization of Volatiles and Petrochemical Base Oils by Photo-Ionization GC×GC-TOF-MS

The characterization of organic mixtures by comprehensive two-dimensional gas chromatography (GC×GC) coupled to electron impact (EI) ionization time-of-flight mass spectrometry (TOF-MS) allows the detection of thousands of compounds. However, owing to the exhaustive fragmentation following EI ionization, despite the use of mass spectral libraries, a majority of the compounds remains unidentified because of the lack of parent ion preservation. Thus, soft-ionization energies leading to organic compounds being ionized with limited or no fragmentation, retaining the molecular ion, has been of interest for many years. In this study, photoionization (PI) was evaluated as the ion source for GC×GC-TOF-MS measurements. First, capabilities and limitations of PI were tested using an authentic mixture of compounds of several chemical classes. Ionization energy exhibited by PI, equivalent to 10.8 eV, resulted in significant retention of molecular ion information; [M]^+• for alkanes, ketones, FAMEs, aromatics, [M-H]^+• for chloroalkanes, and [M-H₂O]^+• for alcohols. Second, considering the potential of PI for hydrocarbons, base oils, complex mixtures of saturated and unsaturated hydrocarbons blended for finished lubricant formulations, were extensively evaluated. Several chemical classes of hydrocarbons were positively identified including a large number of isomeric compounds, both aliphatics and cyclics. Interestingly, branched-alkanes were ionized with lower excess internal energy, not only retaining the molecular ions but also exhibiting unique fragmentation patterns. The results presented herein offer a unique perspective into the detailed molecular characterization of base oils. Such unprecedented identification power of PI coupled with GC×GC-TOF-MS is the first report covering volatiles to low-volatile organic mixtures.

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.

Comprehensive two-dimensional gas chromatography time of flight mass spectrometry (GC×GC-TOFMS) for environmental forensic investigations in developing countries

The disposal and dumping of toxic waste is a matter of growing concern in developing countries, including South Africa. Frequently these countries do not possess access to gas chromatography-high resolution mass spectrometry (GC-HRMS) for the determination of persistent organic pollutants (POPs). This publication describes an alternative approach to the investigation of toxic waste using comprehensive gas chromatography coupled to time of flight mass spectrometry (GC×GC-TOFMS). The technology permits both comprehensive screening of toxic samples for numerous classes of organic pollutants and also quantitative analysis for the individual compounds. This paper describes the use of this technique by analysing samples obtained from a hazardous waste treatment facility in South Africa. After sampling and extraction the samples were analysed for polychlorinated dibenzo-p-dioxins (PCDDs), polychlorinated dibenzofurans (PCDFs) and four dioxin-like non-ortho substituted polychlorinated biphenyls (PCBs). The quantitative values, as well as detection limits, obtained using the GC×GC-TOFMS methodology compares well with those obtained using GC-HRMS; the accepted benchmark technology for this analysis. Although GC×GC-TOFMS is not a target compound analytical technique (as is GC-HRMS), it is possible to obtain information on numerous other classes of organic pollutants present in the samples in one analytical run. This is not possible with GC-HRMS. Several different column combinations have been investigated for handling very complex waste samples and suggestions are presented for the most suitable combination.

Comprehensive two-dimensional gas chromatography-mass spectrometry: a review

Although comprehensive two-dimensional gas chromatography (GC x GC) has been on the scene for more than 15 years, it is still generally considered a relatively novel technique and is yet far from being fully established. The revolutionary aspect of GC x GC, with respect to classical multidimensional chromatography, is that the entire sample is subjected to two distinct analytical separations. The resulting enhanced separating capacity makes this approach a prime choice when GC analysts are challenged with highly complex mixtures. The combination of a third mass spectrometric dimension to a GC x GC system generates the most powerful analytical tool today for volatile and semi-volatile analytes. The present review is focused on the rather brief, but not scant, history of comprehensive two-dimensional GC-MS: the first experiments were carried out at the end of the 1990s and, since then, the methodology has been increasingly studied and applied. Almost all GC x GC-MS applications have been carried out by using either a time-of-flight or quadrupole mass analyzer; significant experiments relative to a variety of research fields, as well as advantages and disadvantages of the MS systems employed, are discussed. The principles, practical and theoretical aspects, and the most significant developments of GC x GC are also described.

Characterisation of middle-distillates by comprehensive two-dimensional gas chromatography (GC×GC): A powerful alternative for performing various standard analysis of middle-distillates

The detailed characterisation of middle distillates is essential for a better understanding of reactions involved in refining process. Owing to higher resolution power and enhanced sensitivity, comprehensive two-dimensional gas chromatography (GC x GC) is a powerful tool for improving characterisation of petroleum samples. The aim of this paper is to compare GC x GC and various ASTM methods -- gas chromatography (GC), liquid chromatography (LC) and mass spectrometry (MS) -- for group type separation and detailed hydrocarbon analysis. Best features of GC x GC are demonstrated and compared to these techniques in terms of cost, time consumption and accuracy. In particular, a new approach of simulated distillation (SimDis-GC x GC) is proposed: compared to the standard method ASTM D2887 it gives unequal information for better understanding of conversion process.