TY - JOUR
T1 - 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
AU - Cozzolino, Daniel
AU - Allder, Katherine
AU - Roumeliotis, Sophia
AU - Eglinton, Jason
N1 - Funding Information:
The authors thank technical staff of the Barley Quality Laboratory, University of Adelaide. This project is supported by Australia's grain growers through their investment body, the Grain Research and Development Corporation , with matching funds from the Australian government.
PY - 2012/11
Y1 - 2012/11
N2 - 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.
AB - 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.
KW - Barley flour
KW - Multivariate data analysis
KW - Principal component analysis
KW - RVA
UR - http://www.scopus.com/inward/record.url?scp=84868193433&partnerID=8YFLogxK
U2 - 10.1016/j.jcs.2012.07.004
DO - 10.1016/j.jcs.2012.07.004
M3 - Article
AN - SCOPUS:84868193433
SN - 0733-5210
VL - 56
SP - 610
EP - 614
JO - Journal of Cereal Science
JF - Journal of Cereal Science
IS - 3
ER -