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Updated: Mar 25, 2026

Chromatographic Fingerprinting by Template Matching for Data Collected by Comprehensive Two-Dimensional Gas Chromatography
Published on: September 2, 2020
Toward automated chromatographic fingerprinting: A non-alignment approach to gas chromatography mass spectrometry
Jochen Vestner1, Gilles de Revel2, Sibylle Krieger-Weber3
1Université de Bordeaux, ISVV, EA 4577, Unité de recherche Œnologie, 33882 Villenave d'Ornon, France; INRA, ISVV, USC 1366 Œnologie, 33882 Villenave d'Ornon, France; Department of Microbiology and Biochemistry, Hochschule Geisenheim University, Von-Lade-Straße 1, 65366 Geisenheim, Germany.
Abstract:
In contrast to targeted analysis of volatile compounds, non-targeted approaches take information of known and unknown compounds into account, are inherently more comprehensive and give a more holistic representation of the sample composition. Although several non-targeted approaches have been developed, there's still a demand for automated data processing tools, especially for complex multi-way data such as chromatographic data obtained from multichannel detectors. This work was therefore aimed at developing a data processing procedure for gas chromatography mass spectrometry (GC-MS) data obtained from non-targeted analysis of volatile compounds. The developed approach uses basic matrix manipulation of segmented GC-MS chromatograms and PARAFAC multi-way modelling. The approach takes retention time shifts and peak shape deformations between samples into account and can be done with the freely available N-way toolbox for MATLAB. A demonstration of the new fingerprinting approach is presented using an artificial GC-MS data set and an experimental full-scan GC-MS data set obtained for a set of experimental wines.
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