Thermodynamic-based retention time predictions of endogenous steroids in comprehensive two-dimensional gas chromatography

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.

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

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

Application of comprehensive 2D chromatography in the anti-doping field: Sample identification and quantification

Anti-doping analysis requires an exceptional level of accuracy and precision given the stakes that are at play. Current methods rely on the application of chromatographic techniques linked with mass spectrometry to provide this. However, despite the effectiveness of these techniques in achieving good selectivity and specificity, some issues still exist. In order to reach the minimum required performance level as set by WADA, labs commonly use selective monitoring by quadrupole mass spectrometry. This can be potentially fooled through the use of masking agents or by moving the peaks, as often only a small portion of the spectrum is used for analysis. Further issues exist in the inability to detect new or modified compounds, or to reanalyse samples/spectra. One technique that could overcome these problems is that of comprehensive 2D chromatography. Here a second separation column is employed to generate greater separative power. Compared to conventional separation, GCxGC allows for a greater peak capacity (i.e., number of peaks that can be resolved within a given time) and greater separation of coeluting compounds, which makes the technique promising for the complex task required in anti-doping. When combined with Time of Flight Mass Spectrometry this technique demonstrates vast potential allowing for full mass range datasets to be obtained for retroactive analysis. Similarly, LCxLC provides improvements in resolving power compared to its 1D counterpart and can be used both online as part of the analysis or offline solely as a purification step. In this review we summarise the work in this field so far, how comprehensive chromatography has been applied to anti-doping studies, and discuss the future application for this technique.

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].