Spectrofluorometric analysis combined with machine learning for geographical and varietal authentication, and prediction of phenolic compound concentrations in red wine

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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.descriptionAvailable online 18 May 2021
dc.description.abstractFluorescence spectroscopy is rapid, straightforward, selective, and sensitive, and can provide the molecular fingerprint of a sample based on the presence of various fluorophores. In conjunction with chemometrics, fluorescence techniques have been applied to the analysis and classification of an array of products of agricultural origin. Recognising that fluorescence spectroscopy offered a promising method for wine authentication, this study investigated the unique use of an absorbance-transmission and fluorescence excitation emission matrix (A-TEEM) technique for classification of red wines with respect to variety and geographical origin. Multi-block data analysis of A-TEEM data with extreme gradient boosting discriminant analysis yielded an unrivalled 100% and 99.7% correct class assignment for variety and region of origin, respectively. Prediction of phenolic compound concentrations with A-TEEM based on multivariate calibration models using HPLC reference data was also highly effective, and overall, the A-TEEM technique was shown to be a powerful tool for wine classification and analysis.
dc.description.statementofresponsibilityRanaweera K.R. Ranaweera, Adam M. Gilmore, Dimitra L. Capone, Susan E.P. Bastian, David W. Jeffery
dc.identifier.citationFood Chemistry, 2021; 361:1-9
dc.identifier.doi10.1016/j.foodchem.2021.130149
dc.identifier.issn0308-8146
dc.identifier.issn1873-7072
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]
dc.identifier.urihttp://hdl.handle.net/2440/131632
dc.language.isoen
dc.publisherElsevier
dc.relation.granthttp://purl.org/au-research/grants/arc/IC170100008
dc.rights© 2021 Elsevier Ltd. All rights reserved.
dc.source.urihttps://doi.org/10.1016/j.foodchem.2021.130149
dc.subjectExtreme gradient boosting
dc.subjectPolyphenols
dc.subjectMulti-block data
dc.subjectAuthenticity
dc.subjectChemometrics
dc.subjectVitis Vinifera
dc.titleSpectrofluorometric analysis combined with machine learning for geographical and varietal authentication, and prediction of phenolic compound concentrations in red wine
dc.typeJournal article
pubs.publication-statusPublished

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