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Mass spectrometry and partial least‐squares regression: a tool for identification of wheat variety and end‐use quality
Author(s) -
Sørensen Helle A.,
Petersen Marianne K.,
Jacobsen Susanne,
Søndergaard Ib
Publication year - 2004
Publication title -
journal of mass spectrometry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.475
H-Index - 121
eISSN - 1096-9888
pISSN - 1076-5174
DOI - 10.1002/jms.626
Subject(s) - partial least squares regression , chemistry , mass spectrometry , regression analysis , regression , mass spectrum , chromatography , analytical chemistry (journal) , biological system , statistics , mathematics , biology
Rapid methods for the identification of wheat varieties and their end‐use quality have been developed. The methods combine the analysis of wheat protein extracts by mass spectrometry with partial least‐squares regression in order to predict the variety or end‐use quality of unknown wheat samples. The whole process takes ∼30 min. Extracts of alcohol‐soluble storage proteins (gliadins) from wheat were analysed by matrix‐assisted laser desorption/ionization time‐of‐flight mass spectrometry. Partial least‐squares regression was subsequently applied using these mass spectra for making models that could predict the wheat variety or end‐use quality. Previously, an artificial neural network was used to identify wheat varieties based on their protein mass spectra profiles. The present study showed that partial least‐squares regression is at least as useful as neural networks for this identification. Furthermore, it was demonstrated that partial least‐squares regression could be used to predict wheat end‐use quality, which has not been possible using neural networks. Copyright © 2004 John Wiley & Sons, Ltd.