Mass spectrometry and partial least-squares regression: a tool for identification of wheat variety and end-use quality
✍ Scribed by Helle A. Sørensen; Marianne K. Petersen; Susanne Jacobsen; Ib Søndergaard
- Publisher
- John Wiley and Sons
- Year
- 2004
- Tongue
- English
- Weight
- 123 KB
- Volume
- 39
- Category
- Article
- ISSN
- 1076-5174
- DOI
- 10.1002/jms.626
No coin nor oath required. For personal study only.
✦ Synopsis
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.