Please use this identifier to cite or link to this item: https://hdl.handle.net/2440/129996
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dc.contributor.authorRanaweera, R.K.R.-
dc.contributor.authorGilmore, A.M.-
dc.contributor.authorCapone, D.L.-
dc.contributor.authorBastian, S.E.P.-
dc.contributor.authorJeffery, D.W.-
dc.date.issued2021-
dc.identifier.citationFood Chemistry, 2021; 335:1-8-
dc.identifier.issn0308-8146-
dc.identifier.issn1873-7072-
dc.identifier.urihttp://hdl.handle.net/2440/129996-
dc.description.abstractWith the increased risk of wine fraud, a rapid and simple method for wine authentication has become a necessity for the global wine industry. The use of fluorescence data from an absorbance and transmission excitation-emission matrix (A-TEEM) technique for discrimination of wines according to geographical origin was investigated in comparison to inductively coupled plasma-mass spectrometry (ICP-MS). The two approaches were applied to commercial Cabernet Sauvignon wines from vintage 2015 originating from three wine regions of Australia, along with Bordeaux, France. Extreme gradient boosting discriminant analysis (XGBDA) was examined among other multivariate algorithms for classification of wines. Models were cross-validated and performance was described in terms of sensitivity, specificity, and accuracy. XGBDA classification afforded 100% correct class assignment for all tested regions using the EEM of each sample, and overall 97.7% for ICP-MS. The novel combination of A-TEEM and XGBDA was found to have great potential for accurate authentication of wines.-
dc.description.statementofresponsibilityRanaweera K.R. Ranaweera, Adam M. Gilmore, Dimitra L. Capone, Susan E.P. Bastian, David W. Jeffery ... et al.-
dc.language.isoen-
dc.publisherElsevier-
dc.rights© 2020 Elsevier Ltd. All rights reserved.-
dc.subjectAuthenticity-
dc.subjectChemometrics-
dc.subjectColour-
dc.subjectExcitation-emission matrix-
dc.subjectFluorescence spectroscopy-
dc.subjectPhenolics-
dc.titleAuthentication of the geographical origin of Australian Cabernet Sauvignon wines using spectrofluorometric and multi-element analyses with multivariate statistical modelling-
dc.typeJournal article-
dc.identifier.doi10.1016/j.foodchem.2020.127592-
dc.relation.granthttp://purl.org/au-research/grants/arc/IC170100008-
pubs.publication-statusPublished-
dc.identifier.orcidRanaweera, R.K.R. [0000-0003-0578-3457]-
dc.identifier.orcidCapone, D.L. [0000-0003-4424-0746]-
dc.identifier.orcidBastian, S.E.P. [0000-0002-8790-2044]-
dc.identifier.orcidJeffery, D.W. [0000-0002-7054-0374]-
Appears in Collections:Agriculture, Food and Wine publications
ARC Training Centre for Innovative Wine Production publications
Aurora harvest 4

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