Title

Model for predicting comprehensive two-dimensional gas chromatography retention times

Document Type

Article

Publication Date

11-16-2007

Publication Title

Journal of Chromatography A

Abstract

A model for approximating the relative retention of solutes in comprehensive two-dimensional gas chromatography (GCxGC) is presented. The model uses retention data from standard single-column temperature-programmed separations. The one-dimensional retention times are first converted into retention indices and then these indices are combined in a simple manner to generate a retention diagram. A retention diagram is an approximation of the two-dimensional chromatogram that has retention order and spacing in both dimensions similar to that found in the experimental chromatogram. If required, the retention diagram can be scaled to more closely resemble the two-dimensional chromatogram. The model has been tested by using retention time data from single-column gas chromatography-mass spectrometry and valve-based GCxGC. A total of 139 volatile organic compounds (VOCs) were examined. Approximately half of the VOCs had a single functional group and a linear alkyl chain (i.e., compounds with the structure Z-(CH(2))(n)-H). The retention diagrams had primary retention orders that were in excellent agreement with the GCxGC chromatograms. The relative secondary retention order for compounds with similar structures was also accurately predicted by the retention diagram. However, the relative secondary retention for compounds with dissimilar structures, such as acyclic alcohols and multi-substituted alkylbenzenes, were less accurately modeled. This study demonstrates how readily available single-column retention time data can be used to provide an a priori estimate of the relative retention of solutes in a GCxGC chromatogram. Such a capability is useful for screening possible combinations of stationary phases.

Volume

1172

Issue

1

First Page

72

Last Page

83

DOI

https://doi.org/10.1016/j.chroma.2007.09.058

ISSN

0021-9673

Comments

ESSN: 1873-3778

Rights

Elsevier

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