Mass spectrometry and partial least-squares regression: a tool for identification of wheat variety and end-use quality.
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.
Sørensen
Helle A
HA
BioCentrum-DTU, Biochemistry and Nutrition Group, Søltofts Plads, Technical University of Denmark, Building 224, DK-2800 Kgs. Lyngby, Denmark.
Petersen
Marianne K
MK
Jacobsen
Susanne
S
Søndergaard
Ib
I
D003718:Denmark; D005504:Food Analysis / Q000379:methods*; D005903:Gliadin / Q000032:analysis; D016018:Least-Squares Analysis; D013058:Mass Spectrometry; D010940:Plant Proteins / Q000032:analysis* / Q000737:chemistry*; D011786:Quality Control; D013045:Species Specificity; D013997:Time Factors; D014908:Triticum / Q000737:chemistry* / Q000145:classification*
D010940:Plant Proteins; D005903:Gliadin