Identification and quantification of homologous series of compound in complex mixtures: autocovariance study of GC/MS chromatograms

Analytical Chemistry
Maria Chiara PietrograndeFrancesco Dondi

Abstract

The paper describes a method for determining homologous classes of compounds in a multicomponent complex chromatogram obtained under programming elution conditions. The method is based on the computation of the autocovariance function of the experimental chromatogram (EACVF). The EACVF plot, if properly interpreted, can be regarded as a "class chromatogram" i.e., a virtual chromatogram formed by peaks whose positions and heights allow identification and quantification of the different homologous series, even if they are embedded in a random complex chromatogram. Theoretical models were developed to describe complex chromatograms displaying random retention pattern, ordered sequences or a combination of them. On the basis of theoretical autocovariance function, the properties of the chromatogram can be experimentally evaluated, under well-defined conditions: in particular, the two components of the chromatogram, ordered and random, can be identified. Moreover, the total number of single components (SCs) and the separated number of the SCs belonging to the random and ordered components can be determined, when the two components display the same concentration. If the mixture contains several homologous series with common frequency...Continue Reading

References

Jul 27, 2001·Journal of Chromatography. a·K Vékey
Nov 28, 2001·Analytical Chemistry·A Felinger, M C Pietrogrande
Jul 25, 2003·Journal of Chromatography. a·I D Wilson, U A Th Brinkman
Jul 30, 2003·Journal of Chromatography. a·Maria Chiara PietrograndeClaire Vidal-Madjar
May 29, 2004·Analytical Chemistry·Nicola MarchettiFrancesco Dondi
Oct 28, 2004·Annali Di Chimica·Maria Chiara PietrograndeFrancesco Dondi

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Citations

Jan 3, 2007·Journal of Mass Spectrometry : JMS
Apr 29, 2009·Journal of Separation Science·Maria Chiara PietrograndeFrancesco Dondi
Mar 14, 2017·Journal of Cheminformatics·Martin Loos, Heinz Singer

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