Multivariate selectivity as a metric for evaluating comprehensive two-dimensional gas chromatography–time-of-flight mass spectrometry subjected to chemometric peak deconvolution

• Published in: Journal of Chromatography A Vol. 1056; no. 1; pp. 145 - 154
• Main Authors: Sinha, Amanda E., Hope, Janiece L., Prazen, Bryan J., Fraga, Carlos G., Nilsson, Erik J., Synovec, Robert E.
• Format: Journal Article Conference Proceeding
• Language: English
• Published: Amsterdam Elsevier B.V 12.11.2004; Elsevier
• Subjects: Analytical chemistry; Chemistry; Chemometrics; Chromatographic methods and physical methods associated with chromatography; Deconvolution; Evaluation Studies as Topic; Exact sciences and technology; Gas chromatographic methods; Gas chromatography, comprehensive two-dimensional; Gas Chromatography-Mass Spectrometry - methods; Multivariate selectivity; Net analyte signal; Parallel factor analysis; Plant Extracts - chemistry; Sensitivity and Specificity; Multivariate selectivity; Deconvolution; Net analyte signal; Parallel factor analysis; Chemometrics; Gas chromatography, comprehensive two-dimensional; Chemical analysis; Factor analysis; Two dimensional chromatography; Multicomponent mixture; Coupled method; comprehensive two-dimensional; Net analyte signal; Multivariate selectivity; Selectivity; Parallel; Gas chromatography; Parallel factor analysis; Chemometrics; Deconvolution; Gas chromatography; Time of flight method; Mass spectrometry
• ISSN: 0021-9673
• DOI: 10.1016/j.chroma.2004.06.110

Two-dimensional gas chromatography (GC × GC) coupled to time-of-flight mass spectrometry (TOFMS) [GC × GC–TOFMS)] is a highly selective technique well suited to analyzing complex mixtures. The data generated is information-rich, making it applicable to multivariate quantitative analysis and pattern recognition. One separation on a GC × GC–TOFMS provides retention times on two chromatographic columns and a complete mass spectrum for each component within the mixture. In this report, we demonstrate how GC × GC–TOFMS combined with trilinear chemometric techniques, specifically parallel factor analysis (PARAFAC) initiated by trilinear decomposition (TLD), results in a powerful analytical methodology for multivariate deconvolution. Using PARAFAC, partially resolved components in complex mixtures can be deconvoluted and identified without requiring a standard data set, signal shape assumptions or any fully selective mass signals. A set of four isomers ( iso-butyl, sec-butyl, tert-butyl, and n-butyl benzenes) is used to investigate the practical limitations of PARAFAC for the deconvolution of isomers at varying degrees of chromatographic resolution and mass spectral selectivity. In this report, multivariate selectivity was tested as a metric for evaluating GC × GC–TOFMS data that is subjected to PARAFAC peak deconvolution. It was found that deconvolution results were best with multivariate selectivities over 0.18. Furthermore, the application of GC × GC–TOFMS followed by TLD/PARAFAC is demonstrated for a plant metabolite sample. A region of GC × GC–TOFMS data from a complex natural sample of a derivatized metabolic plant extract from Huilmo ( Sisyrinchium striatum) was analyzed using TLD/PARAFAC, demonstrating the utility of this analytical technique on a natural sample containing overlapped analytes without selective ions or peak shape assumptions.