Chemometrics, Comprehensive Two-Dimensional gas chromatography and “omics” sciences: Basic tools and recent applications

The advent of Comprehensive Two-dimensional Gas Chromatography (GC × GC) as a practical and accessible analytical tool had a considerable impact on analytical procedures associated to the so-called “omics” sciences. Specially when GC × GC is hyphenated to mass spectrometers or other multichannel det...

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Bibliographic Details
Published in:	TrAC, Trends in analytical chemistry (Regular ed.) Vol. 134; p. 116111
Main Authors:	Pollo, Breno Jorge, Teixeira, Carlos Alberto, Belinato, Joao Raul, Furlan, Mayra Fontes, Cunha, Isabela Cristina de Matos, Vaz, Caroline Rocha, Volpato, Gustavo Veronezi, Augusto, Fabio
Format:	Journal Article
Language:	English
Published:	Elsevier B.V 01-01-2021
Subjects:	Big data Data mining Foodomics GC×GC Metabolomics Multivariate analysis Petroleomics Metabolomics Big data Multivariate analysis Data mining GC×GC Petroleomics Foodomics
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Summary:	The advent of Comprehensive Two-dimensional Gas Chromatography (GC × GC) as a practical and accessible analytical tool had a considerable impact on analytical procedures associated to the so-called “omics” sciences. Specially when GC × GC is hyphenated to mass spectrometers or other multichannel detectors, in a single run it is possible to separate, detect and identify up to thousands of metabolites. However, the resulting data sets are exceedingly complex, and retrieving proper biochemical information from them demands powerful statistical tools to deal effectively with the massive amount of information generated by GC × GC. Nevertheless, the obtention of results valid on a chemical and biological standpoint depends on a deep understanding by the analyst of the fundamentals both of GC × GC and chemometrics. This review focuses on the basics of contemporary, fundamental chemometric tools applied to proccessing of GC × GC obtained from metabolomic, petroleomic and foodomic analyses. Here, we described the fundamentals of pattern recognition methods applied to GC × GC. Also, we explore how different detectors affect data structure and approaches for better data handling. Limitations regarding data structure and deviations from linearity are stressed for each algorithm, as well as their typical applications and expected output. •Chemometrics allows trend recognition that would not be discernible using manual assessment.•GC × GC data structure, pre-processing steps and algorithms restrictions are discussed.•Chemometrics strategies applied in omics fields and its misuse are evaluated.
ISSN:	0165-9936 1879-3142
DOI:	10.1016/j.trac.2020.116111