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

Helle Aagaard Sørensen, Marianne Kjerstine Petersen, Susanne Jacobsen, Ib Søndergaard

    Research output: Contribution to journalJournal articleResearchpeer-review

    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 similar to30 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.
    Original languageEnglish
    JournalJournal of Mass Spectrometry
    Volume39
    Pages (from-to)607-612
    Publication statusPublished - 2004

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