File(s) not publicly available

Combining partial least squares (PLS) discriminant analysis and rapid visco analyser (RVA) to classify barley samples according to year of harvest and locality

journal contribution
posted on 01.11.2018, 00:00 by Daniel CozzolinoDaniel Cozzolino, S Roumeliotis, J Eglinton
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. © 2013 Springer Science+Business Media New York.

Funding

Category 2 - Other Public Sector Grants Category

History

Volume

7

Issue

4

Start Page

887

End Page

892

Number of Pages

6

eISSN

1936-976X

ISSN

1936-9751

Publisher

Springer New York LLC

Peer Reviewed

Yes

Open Access

No

Acceptance Date

06/08/2013

External Author Affiliations

University of Adelaide

Era Eligible

Yes

Journal

Food Analytical Methods