Mass spectrometry and partial least-squares regression: a tool for identification of wheat variety and end-use quality

Journal of Mass Spectrometry : JMS
Helle A SørensenIb Søndergaard

Abstract

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 approximately 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.

Citations

Jan 19, 2005·Rapid Communications in Mass Spectrometry : RCM·Andrea GhirardoIb Søndergaard
Dec 19, 2007·Journal of Mass Spectrometry : JMS·Pasquale FerrantiFrancesco Addeo
Oct 16, 2009·Journal of Mass Spectrometry : JMS·Gianfranco MamonePasquale Ferranti
Jun 1, 2011·European Journal of Mass Spectrometry·Donatella AielloAntonio Tagarelli
Oct 29, 2004·Phytochemical Analysis : PCA
Nov 30, 2007·Proteomics·Fran SupekTomislav Smuc
May 10, 2008·European Journal of Gastroenterology & Hepatology·Alberto HernandoEnrique Méndez
Nov 26, 2005·Expert Review of Proteomics·Eric T FungFujun Zhang

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