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 (tr) 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 (2tr). 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 tr predictions are performed on both dimensions using the same flow rate as the one deployed in the conventional GC × GC system. The separate tr 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 2tr of all investigated analytes, providing an average 2tr modeling error of 0.26 ± 0.01 s.

Medienart:

E-Artikel

Erscheinungsjahr:

2022

Erschienen:

2022

Enthalten in:

Zur Gesamtaufnahme - volume:94

Enthalten in:

Analytical chemistry - 94(2022), 49 vom: 13. Dez., Seite 17081-17089

Sprache:

Englisch

Beteiligte Personen:

Gaida, Meriem [VerfasserIn]
Franchina, Flavio A [VerfasserIn]
Stefanuto, Pierre-Hugues [VerfasserIn]
Focant, Jean-François [VerfasserIn]

Links:

Volltext

Themen:

Journal Article
Research Support, Non-U.S. Gov't

Anmerkungen:

Date Completed 14.12.2022

Date Revised 21.12.2022

published: Print-Electronic

Citation Status MEDLINE

doi:

10.1021/acs.analchem.2c03107

funding:

Förderinstitution / Projekttitel:

PPN (Katalog-ID):

NLM349578427