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

Abstract

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 (2 t 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 2 t r of all investigated analytes, providing an average 2 t r modeling error of 0.26 ± 0.01 s.

Comprehensive two‐dimensional gas chromatography— A discussion on recent innovations

“…Predicting 2 D retention times is thought to be more challenging than predicting 1D retention times [136,137]. In part, this is due to variations introduced by the modulator, which are not accounted for in the retention models.…”

“…Gaida et al. [136] considered the two underlying 1DGC systems individually to predict GC × GC behavior in what they call a “top‐down” approach. The modulator was considered an injection device for the 2 D separation.…”

Theoretical modeling and machine learning-based data processing workflows in comprehensive two-dimensional gas chromatography—A review

“…In this context, separate 1D-GC retention time predictions are conducted and then combined to account for the GC × GC separation space. This system-independent approach gave favorable results in terms of separation space description and occupancy [18,68].…”

“…Specifically, because it is difficult to assess the temperature variations that occur within the modulator as a result of the constant cooling and heating processes. This dilemma, in part, explains why fewer modeling works are conducted using thermal modulation [18,33,70,[79][80][81][82]. The thermal modulation of GC × GC systems is often viewed as a dynamic process.…”

“…Additionally, it can guide the selection of appropriate column sets that help to achieve a good orthogonal separation mechanism [16]. Furthermore, theoretical modeling can aid in the prediction of retention times and peak shapes of analytes in complex mixtures, facilitating the potential identification of unknown compounds [17,18]. Ultimately, these advantages aim to optimize the separation conditions to achieve the best possible separation space occupation.…”