Modelling Cabernet-Sauvignon wine sensory traits from spectrofluorometric data

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dc.contributor.authorSouza Gonzaga, L.
dc.contributor.authorBastian, S.E.P.
dc.contributor.authorCapone, D.L.
dc.contributor.authorRanaweera, R.K.R.
dc.contributor.authorJeffery, D.W.
dc.date.issued2021
dc.description.abstractUnderstanding how wine compositional traits can be related to sensory profiles is an important and ongoing challenge. Enhancing knowledge in this area could assist producers to select practices that deliver wines of the desired style and sensory specifications. This work reports the use of spectrofluorometry in conjunction with chemometrics for prediction, correlation, and classification based on sensory descriptors obtained using a rate-all-that-apply sensory assessment of Cabernet-Sauvignon wines (n = 26). Sensory results were first subjected to agglomerative hierarchical cluster analysis, which separated the wines into five clusters represented by different sensory profiles. The clusters were modelled in conjunction with excitation-emission matrix (EEM) data from fluorescence measurements using extreme gradient boosting discriminant analysis. This machine learning technique was able to classify the wines into the pre-defined sensory clusters with 100 % accuracy. Parallel factor analysis of the EEMs identified four main fluorophore components that were tentatively assigned as catechins, phenolic aldehydes, anthocyanins, and resveratrol (C1, C2, C3, and C4, respectively). Association of these four components with different sensory descriptors was possible through multiple factor analysis, with C1 relating to ‘dark fruits’ and ‘savoury’, C2 with ‘barnyard’, C3 with ‘cooked vegetables’ and ‘vanilla/chocolate’, and C4 with ‘barnyard’ and a lack of C1 descriptors. Partial least squares regression modelling was undertaken with EEM data and sensory results, with a model for perceived astringency being able to predict the panel scores with 68.1 % accuracy. These encouraging outcomes pave the way for further studies that relate sensory traits to fluorescence data and move research closer to the ultimate goal of predicting wine sensory expression from a small number of compositional factors.
dc.description.statementofresponsibilityLira Souza Gonzaga, Susan E.P. Bastian, Dimitra L. Capone, Ranaweera K.R. Ranaweera and David W. Jeffery
dc.identifier.citationOENO One, 2021; 55(4):19-33
dc.identifier.doi10.20870/oeno-one.2021.55.4.4805
dc.identifier.issn2494-1271
dc.identifier.issn2494-1271
dc.identifier.orcidSouza Gonzaga, L. [0000-0002-0191-191X]
dc.identifier.orcidBastian, S.E.P. [0000-0002-8790-2044]
dc.identifier.orcidCapone, D.L. [0000-0003-4424-0746]
dc.identifier.orcidRanaweera, R.K.R. [0000-0003-0578-3457]
dc.identifier.orcidJeffery, D.W. [0000-0002-7054-0374]
dc.identifier.urihttps://hdl.handle.net/2440/134533
dc.language.isoen
dc.publisherInternational Viticulture and Enology Society - IVES
dc.relation.granthttp://purl.org/au-research/grants/arc/IC170100008
dc.rights© 2021 International Viticulture and Enology Society – IVES. This article is published under the Creative Commons licence (CC BY 4.0). Use of all or part of the content of this article must mention the authors, the year of publication, the title, the name of the journal, the volume, the pages and the DOI in compliance with the information given above.
dc.source.urihttps://doi.org/10.20870/oeno-one.2021.55.4.4805
dc.subjectRate-all-that-apply; cluster analysis; excitation-emission matrix; partial least squares regression; machine learning; chemometrics
dc.titleModelling Cabernet-Sauvignon wine sensory traits from spectrofluorometric data
dc.typeJournal article
pubs.publication-statusPublished

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