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Type: Journal article
Title: Spectrofluorometric analysis combined with machine learning for geographical and varietal authentication, and prediction of phenolic compound concentrations in red wine
Author: Ranaweera, R.K.R.
Gilmore, A.M.
Capone, D.L.
Bastian, S.E.P.
Jeffery, D.W.
Citation: Food Chemistry, 2021; 361:1-9
Publisher: Elsevier
Issue Date: 2021
ISSN: 0308-8146
Statement of
Ranaweera K.R. Ranaweera, Adam M. Gilmore, Dimitra L. Capone, Susan E.P. Bastian, David W. Jeffery
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.
Keywords: Extreme gradient boosting
Multi-block data
Vitis Vinifera
Description: Available online 18 May 2021
Rights: © 2021 Elsevier Ltd. All rights reserved.
DOI: 10.1016/j.foodchem.2021.130149
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Appears in Collections:Agriculture, Food and Wine publications
ARC Training Centre for Innovative Wine Production publications
Aurora harvest 8

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