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

被引:64
作者
Sinha, AE
Hope, JL
Prazen, BJ
Fraga, CG
Nilsson, EJ
Synovec, RE
机构
[1] Univ Washington, Ctr Proc Analyt Chem, Dept Chem, Seattle, WA 98195 USA
[2] USAF Acad, Dept Chem, Colorado Springs, CO 80840 USA
[3] Insilicos, Seattle, WA 98103 USA
关键词
parallel factor analysis; chemometrics; deconvolution; gas chromatography; comprehensive two-dimensional; net analyte signal; multivariate selectivity;
D O I
10.1016/j.chroma.2004.06.110
中图分类号
Q5 [生物化学];
学科分类号
071010 ; 081704 ;
摘要
Two-dimensional gas chromatography (GC x GC) coupled to time-of-flight mass spectrometry (TOFMS) [GC x 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 x 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 x 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 FARAFAC 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 x 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 x GC-TOFMS followed by TLD/PARAFAC is demonstrated for a plant metabolite sample. A region of GC x 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. (C) 2004 Elsevier B.V. All rights reserved.
引用
收藏
页码:145 / 154
页数:10
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