Facile Approach for Calculation of Second Dimensional Retention Indices in Comprehensive Two Dimensional Gas Chromatography with Single Injection

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.

Modeling of the first dimensional peak with two modulated sub-peaks in comprehensive two-dimensional gas chromatography

According to previous published works, precise modeling of the first dimensional (¹D) peak in comprehensive two-dimensional gas chromatography (GC × GC) requires at least 3 modulated sub-peaks (MSP). This requirement is sometimes difficult to meet, e.g., in case of undersampling modulation. In the present work, the feasibility of modeling of the ¹D peak with only 2 MSP was demonstrated. The effects of modulation phase (ϕ), modulation period (P_M), the peak width (¹σ), and the peak shape of the original ¹D peak on the accuracy of the proposed method were explored. When employing P_M ranging from 6 s ~ 3 s to modulate original peaks with ¹σ = 1.2 s ~ 0.6 s, the maximal error of the modeled ¹t_R is 1.08 s, which is far less than the error generated by employing the largest MSP to estimate the ¹t_R. The deviation of modeled ¹t_R increases with the increase of peak shape distortion, and this deviation is ≤ 0.67 s when tailing factor (T_f) in the range of 0.8 to 1.5. The application of the proposed method was demonstrated by assisting identification of a monoterpene in Myrrh sample. The proposed approach could improve the accuracy in calculation of ¹t_R or ¹I and enhance the reliability of compound identification in GC × GC analysis with undersampling modulation.

A unique data analysis framework and open source benchmark data set for the analysis of comprehensive two-dimensional gas chromatography software

Comprehensive two-dimensional gas chromatography (GC × GC) is amongst the most powerful separation technologies currently existing. Since its advent in early 1990, it has become an established method which is readily available. However, one of its most challenging aspects, especially in hyphenation with mass spectrometry is the high amount of chemical information it provides for each measurement. The GC × GC community agrees that there, the highest demand for action is found. In response, the number of software packages allowing for in-depth data processing of GC × GC data has risen over the last couple of years. These packages provide sophisticated tools and algorithms allowing for more streamlined data evaluation. However, these tools/algorithms and their respective specific functionalities differ drastically within the available software packages and might result in various levels of findings if not appropriately implemented by the end users. This study focuses on two main objectives. First, to propose a data analysis framework and second to propose an open-source dataset for benchmarking software options and their specificities. Thus, allowing for an unanimous and comprehensive evaluation of GC × GC software. Thereby, the benchmark data includes a set of standard compound measurements and a set of chocolate aroma profiles. On this foundation, eight readily available GC × GC software packages were anonymously investigated for fundamental and advanced functionalities such as retention and detection device derived parameters, revealing differences in the determination of e.g. retention times and mass spectra.