Considerations for the automated collection of thermodynamic data in 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.

Retention Database for Prediction, Simulation, and Optimization of GC Separations

This work presents an open source database with suitable retention parameters for prediction and simulation of GC separations and gives a short introduction to three common retention models. Useful computer simulations play an important role to save resources and time in method development in GC. Thermodynamic retention parameters for the ABC model and the K-centric model are determined by isothermal measurements. This standardized procedure of measurements and calculations, presented in this work, have a useful benefit for all chromatographers, analytical chemists, and method developers because it can be used in their own laboratories to simplify the method development. The main benefits as simulations of temperature-programed GC separations are demonstrated and compared to measurements. The observed deviations of predicted retention times are in most cases less than 1%. The database includes more than 900 entries with a large range of compounds such as VOCs, PAHs, FAMEs, PCBs, or allergenic fragrances over 20 different GC columns.

The effect of column and temperature variation on the determination of the dead time in gas chromatographic systems using indirect methods

The precise determination of the dead time is essential in a chromatography system as it is a primary parameter for the determination of other secondary parameters such as the adjusted retention time, relative retention time, retention factor and retention index. Several of the indirect methods used for the determination of the dead time in this study were iteration, nonlinear, spreadsheet and statistics methods, which were implemented using the ANSI C programming language. The calculation of each method was tested with temperature and column variations for measuring the retention time of a n-alkane homologous series and accuracy analysis of each mathematical method (indirect method) to the marker substance (direct method). Changes in the temperature and column variations (type, polarity and column length) affected the calculation of the dead time values but did not affect its accuracy. The value of the dead time generated by the non-linear method was relatively high, with errors above 10%, while the other methods utilized are quite good with errors below 8% regardless of the column and temperature variations.