Mass spectrometry and partial least-squares regression: a tool for identification of wheat variety and end-use quality |
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Authors: | Sørensen Helle A Petersen Marianne K Jacobsen Susanne Søndergaard Ib |
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Institution: | BioCentrum-DTU, Biochemistry and Nutrition Group, Søltofts Plads, Technical University of Denmark, Building 224, DK-2800 Kgs. Lyngby, Denmark |
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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. |
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Keywords: | wheat quality variety mass spectrometry partial least-squares regression |
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