Please use this identifier to cite or link to this item: https://hdl.handle.net/2440/86753
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Type: Journal article
Title: Combining partial least squares (PLS) discriminant analysis and Rapid Visco Analyser (RVA) to classify barley samples according to year of harvest and locality
Author: Cozzolino, D.
Roumeliotis, S.
Eglinton, J.
Citation: Food Analytical Methods, 2014; 7(4):887-892
Publisher: Springer US
Issue Date: 2014
ISSN: 1936-9751
1936-976X
Statement of
Responsibility: 
D. Cozzolino, S. Roumeliotis, J. Eglinton
Abstract: The aim of this study was to evaluate the usefulness of the Rapid Visco Analyser (RVA) instrument combined with pattern recognition methods as tools to differentiate commercial barley samples from two South Australian localities and three harvests. Principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA) and stepwise discriminant analysis were applied to classify samples based on the RVA profiles using full cross validation (leave-one-out) as the validation method. The PLS-DA models correctly classify 96.3 and 97.8 % of the barley samples according to harvest and locality, using the profiles generated by the RVA instrument. Analysis and interpretation of the eigenvectors and loadings from the PCA or PLS-DA models developed verified that the RVA profiles contain relevant information related to starch pasting properties that allows sample classification. These results suggest that RVA coupled with PLS-DA holds necessary information for a successful classification of barley samples sourced from different localities and harvests.
Keywords: Barley; RVA; Starch; Principal component analysis; Discriminant analysis
Rights: © Springer Science+Business Media New York 2013
DOI: 10.1007/s12161-013-9696-3
Appears in Collections:Agriculture, Food and Wine publications
Aurora harvest 2

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