Fluorescence fingerprinting to predict multiple chromatic and phenolic parameters for a diversified set of red wines.

Absorbance-transmittance and fluorescence excitation-emission matrix (A-TEEM) spectroscopy was utilised to study red wines (n = 113) undergoing maturation or bottle ageing. Chromatic and phenolic parameters including wine colour density, hue, monomeric pigments, various forms of polymeric pigments, tannin concentration using precipitation assays, SO2 resistant pigments, total anthocyanins, and total phenolics were modelled. Machine learning models using extreme gradient boosting (XGB) regression were developed for EEM data and fused A-TEEM data based on reference chemistry, achieving high R2 and low RMSE values of Train and cross-validation (CV) models across all parameters. XGBR Test models based on the A-TEEM dataset showed enhanced prediction accuracy for certain parameters than using EEMs alone. Overall, this study provided insights into effectively determining colour and phenolic profile of wines with a rapid fluorescence spectroscopy approach, offering a valuable tool that could be used to facilitate research, wine quality control, and decision-making in wineries.