Feasibility study on the use of multivariate data methods and derivatives to enhance information from barley flour and malt samples analysed using the Rapid Visco Analyser

Cozzolino, D, Allder, K, Roumeliotis, S and Eglinton, J 2012, 'Feasibility study on the use of multivariate data methods and derivatives to enhance information from barley flour and malt samples analysed using the Rapid Visco Analyser', Journal of Cereal Science 2012 Elsevier Ltd, vol. 56, no. 3, pp. 610-614.


Document type: Journal Article
Collection: Journal Articles

Title Feasibility study on the use of multivariate data methods and derivatives to enhance information from barley flour and malt samples analysed using the Rapid Visco Analyser
Author(s) Cozzolino, D
Allder, K
Roumeliotis, S
Eglinton, J
Year 2012
Journal name Journal of Cereal Science 2012 Elsevier Ltd
Volume number 56
Issue number 3
Start page 610
End page 614
Total pages 5
Publisher Academic Press
Abstract In order to extend the use of the Rapid Visco Analyser (RVA) as an analytical tool in barley breeding programs, it is necessary to find relationships between barley flour pasting properties and potential malting quality. Traditionally, the RVA is used to provide discrete values related with the pasting characteristics of the sample under analysis. Although this approach is very useful, considering the rich data generated by RVA analysis, this can result in the loss of information about starch pasting characteristics, reducing the potential of the RVA as an analytical tool. This study aims to evaluate the ability of using multivariate data methods (MVA) and derivatives to the profile generated by the RVA as a source of information to further study starch pasting characteristics to select materials in barley breeding programs or other food applications. The use of MVA techniques such as principal component analysis (PCA) and partial least squares (PLS) regression together with the use of derivatives (e.g. first and second derivatives) allows better interpretation of the RVA profile, resulting in more information related to the pasting properties of the sample.
Subject Food Sciences not elsewhere classified
Keyword(s) RVA
Barley flour
Principal component analysis
Multivariate data analysis
DOI - identifier 10.1016/j.jcs.2012.07.004
Copyright notice © 2012 Elsevier Ltd
ISSN 0733-5210
Versions
Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 17 times in Thomson Reuters Web of Science Article | Citations
Scopus Citation Count Cited 0 times in Scopus Article
Altmetric details:
Access Statistics: 9 Abstract Views  -  Detailed Statistics
Created: Mon, 29 Apr 2019, 13:04:00 EST by Catalyst Administrator
© 2014 RMIT Research Repository • Powered by Fez SoftwareContact us