Mass spectrometry and partial least-squares regression: a tool for identification of wheat variety and end-use quality.
J Mass Spectrom
; 39(6): 607-12, 2004 Jun.
Article
en En
| MEDLINE
| ID: mdl-15236298
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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Colección:
01-internacional
Banco de datos:
MEDLINE
Asunto principal:
Proteínas de Plantas
/
Triticum
/
Análisis de los Alimentos
Tipo de estudio:
Diagnostic_studies
/
Prognostic_studies
País/Región como asunto:
Europa
Idioma:
En
Revista:
J Mass Spectrom
Año:
2004
Tipo del documento:
Article
País de afiliación:
Dinamarca