Absorbance-transmission and fluorescence excitation-emission matrix (A-TEEM) with multi-block data analysis and machine learning for accurate intraregional classification of Barossa Shiraz wine

Abstract

Authentication of wine can be considered at different scales, with classification according to country, province/state, or appellation/wine producing region. An absorbance-transmission and excitation-emission matrix (A-TEEM) technique was applied for the first time to examine intraregional differences, using Shiraz wines (n = 186) produced during three vintages from five subregions of Barossa Valley and from Eden Valley. Absorption spectra and EEM fingerprints were modelled as a multi-block data set for initial exploration with k-means cluster analysis and principal component analysis, and then with machine learning modelling using extreme gradient boosting discriminant analysis (XGBDA). Whereas some clustering was evident with the initial unsupervised approaches, classification with XGBDA afforded an impressive 100% correct class assignment for subregion and vintage year. Extending the utility and novelty of the A-TEEM approach, predictive models for chemical parameters (alcohol, glucose + fructose, pH, titratable acidity, and volatile acidity) were also validated using A-TEEM data with XGB regression.