Application of sequential and orthogonalised-partial least squares (SO-PLS) regression to predict sensory properties of Cabernet Sauvignon wines from grape chemical composition

Simple item page
dc.contributor.authorNiimi, J.
dc.contributor.authorTomic, O.
dc.contributor.authorNæs, T.
dc.contributor.authorJeffery, D.W.
dc.contributor.authorBastian, S.E.P.
dc.contributor.authorBoss, P.K.
dc.date.issued2018
dc.description.abstractThe current study determined the applicability of sequential and orthogonalised-partial least squares (SO-PLS) regression to relate Cabernet Sauvignon grape chemical composition to the sensory perception of the corresponding wines. Grape samples (n = 25) were harvested at a similar maturity and vinified identically in 2013. Twelve measures using various (bio)chemical methods were made on grapes. Wines were evaluated using descriptive analysis with a trained panel (n = 10) for sensory profiling. Data was analysed globally using SO-PLS for the entire sensory profiles (SO-PLS2), as well as for single sensory attributes (SO-PLS1). SO-PLS1 models were superior in validated explained variances than SO-PLS2. SO-PLS provided a structured approach in the selection of predictor chemical data sets that best contributed to the correlation of important sensory attributes. This new approach presents great potential for application in other explorative metabolomics studies of food and beverages to address factors such as quality and regional influences.
dc.description.statementofresponsibilityJun Niimi, Oliver Tomic, Tormod Næs, David W.Jeffery, Susan E.P.Bastian, Paul K.Boss
dc.identifier.citationFood Chemistry, 2018; 256:195-202
dc.identifier.doi10.1016/j.foodchem.2018.02.120
dc.identifier.issn0308-8146
dc.identifier.issn1873-7072
dc.identifier.orcidNiimi, J. [0000-0002-2642-283X]
dc.identifier.orcidJeffery, D.W. [0000-0002-7054-0374]
dc.identifier.orcidBastian, S.E.P. [0000-0002-8790-2044]
dc.identifier.orcidBoss, P.K. [0000-0003-0356-9342]
dc.identifier.urihttp://hdl.handle.net/2440/121145
dc.language.isoen
dc.publisherElsevier
dc.rights© 2018 Elsevier Ltd. All rights reserved.
dc.source.urihttps://doi.org/10.1016/j.foodchem.2018.02.120
dc.subjectMulti-block data analysis; data orthogonalisation; grape; wine; sensory
dc.titleApplication of sequential and orthogonalised-partial least squares (SO-PLS) regression to predict sensory properties of Cabernet Sauvignon wines from grape chemical composition
dc.typeJournal article
pubs.publication-statusPublished

Files

Collections

Agriculture, Food and Wine publications