Spectrofluorometric analysis to trace the molecular fingerprint of wine during the winemaking process and recognise the blending percentage of different varietal wines

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dc.contributor.authorRanaweera, R.K.R.
dc.contributor.authorGilmore, A.M.
dc.contributor.authorBastian, S.E.P.
dc.contributor.authorCapone, D.
dc.contributor.authorJeffery, D.
dc.date.issued2022
dc.descriptionPublished: 10 March 2022
dc.description.abstractAs a robust analytical method, spectrofluorometric analysis with machine learning modelling has recently been used to authenticate wine from different regions, vintages and varieties. This preliminary study investigated whether the molecular fingerprint obtained with this approach is maintained throughout the winemaking process, along with assessing different percentages of wine in a blend. Monovarietal wine samples were collected at different stages of the winemaking process and analysed with the absorbance-transmission and fluorescence excitation-emission matrix (A-TEEM) technique. Wines were clustered tightly according to origin for the different winemaking stages, with some clear separation of different regions and varieties based on principal component analysis. In addition, wines were classified with 100 % accuracy according to varietal origin using extreme gradient boosting (XGB) discriminant analysis. The sensitivity of the A-TEEM technique was such that it allowed for accurate modelling of wine blends containing as little as 1 % of Cabernet-Sauvignon or Grenache in Shiraz wine when employing XGB regression, which performed better than partial least squares regression. The overall results indicated the potential for applying A-TEEM and machine learning modelling to wine chemical traceability through production to guarantee the provenance of wine or identify the composition of a blend.
dc.description.statementofresponsibilityRanaweera K.R. Ranaweera, Adam M. Gilmore, Susan E.P. Bastian, Dimitra L. Capone and David W. Jeffery
dc.identifier.citationOENO One, 2022; 56(1):189-196
dc.identifier.doi10.20870/oeno-one.2022.56.1.4904
dc.identifier.issn2494-1271
dc.identifier.issn2494-1271
dc.identifier.orcidRanaweera, R.K.R. [0000-0003-0578-3457]
dc.identifier.orcidBastian, S.E.P. [0000-0002-8790-2044]
dc.identifier.orcidCapone, D. [0000-0003-4424-0746]
dc.identifier.orcidJeffery, D. [0000-0002-7054-0374]
dc.identifier.urihttps://hdl.handle.net/2440/134705
dc.language.isoen
dc.publisherInternational Viticulture and Enology Society - IVES
dc.relation.granthttp://purl.org/au-research/grants/arc/IC170100008
dc.rights© 2022 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.2022.56.1.4904
dc.subjectAuthenticity; excitation-emission matrix; traceability; chemometrics; vinification
dc.titleSpectrofluorometric analysis to trace the molecular fingerprint of wine during the winemaking process and recognise the blending percentage of different varietal wines
dc.typeJournal article
pubs.publication-statusPublished

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