Toward automated chromatographic fingerprinting: A non-alignment approach to gas chromatography mass spectrometry data

• A novel data processing procedure for non-targeted gas chromatography mass spectrometry (GC–MS) data is proposed.
• Basic matrix manipulation of segmented GC–MS chromatograms and PARAFAC multi-way modelling is used.
• Retention time shifts and peak shape deformations between samples are taken into account.
• The procedure is demonstrated on an artificial and an experimental full-scan GC–MS data set.

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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