Authentication of the geographical origin of Australian Cabernet Sauvignon wines using spectrofluorometric and multi-element analyses with multivariate statistical modelling

Ranaweera K R Ranaweera; Adam M Gilmore; Dimitra L Capone; Susan E P Bastian; David W Jeffery

doi:10.1016/j.foodchem.2020.127592

Authentication of the geographical origin of Australian Cabernet Sauvignon wines using spectrofluorometric and multi-element analyses with multivariate statistical modelling

Food Chem. 2021 Jan 15:335:127592. doi: 10.1016/j.foodchem.2020.127592. Epub 2020 Jul 17.

Authors

Ranaweera K R Ranaweera¹, Adam M Gilmore², Dimitra L Capone³, Susan E P Bastian³, David W Jeffery⁴

Affiliations

¹ Department of Wine and Food Science, and Waite Research Institute, The University of Adelaide (UA), PMB 1, Glen Osmond, South Australia 5064, Australia.
² HORIBA Instruments Inc., 20 Knightsbridge Rd., Piscataway, NJ 08854, United States.
³ Department of Wine and Food Science, and Waite Research Institute, The University of Adelaide (UA), PMB 1, Glen Osmond, South Australia 5064, Australia; Australian Research Council Training Centre for Innovative Wine Production, UA, PMB 1, Glen Osmond, South Australia 5064, Australia.
⁴ Department of Wine and Food Science, and Waite Research Institute, The University of Adelaide (UA), PMB 1, Glen Osmond, South Australia 5064, Australia; Australian Research Council Training Centre for Innovative Wine Production, UA, PMB 1, Glen Osmond, South Australia 5064, Australia. Electronic address: david.jeffery@adelaide.edu.au.

PMID: 32750629
DOI: 10.1016/j.foodchem.2020.127592

Abstract

With 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.

Keywords: Authenticity; Chemometrics; Colour; Excitation-emission matrix; Fluorescence spectroscopy; Phenolics.

MeSH terms

Australia
Geography*
Models, Statistical
Spectrometry, Fluorescence
Wine / analysis*