Spectrofluorometric analysis combined with machine learning for geographical and varietal authentication, and prediction of phenolic compound concentrations in red wine
Date
2021
Authors
Ranaweera, R.K.R.
Gilmore, A.M.
Capone, D.L.
Bastian, S.E.P.
Jeffery, D.W.
Editors
Advisors
Journal Title
Journal ISSN
Volume Title
Type:
Journal article
Citation
Food Chemistry, 2021; 361:1-9
Statement of Responsibility
Ranaweera K.R. Ranaweera, Adam M. Gilmore, Dimitra L. Capone, Susan E.P. Bastian, David W. Jeffery
Conference Name
Abstract
Fluorescence 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.
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Dissertation Note
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Available online 18 May 2021
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© 2021 Elsevier Ltd. All rights reserved.