LAUSR

ABSTRACT Thirty samples of Cabernet Sauvignon Mexican wines from 5 wine regions, 19 wineries and vintage between 2004 and 2012 were characterised regarding total phenolic compounds (TPC), anthocyanins (AC), tannins (TC), flavonoids (FC) and antioxidant capacity (ABTS and DPPH assays). Samples were processed using Fourier transform mid-infrared spectroscopy combined with partial least squares regression (PLS1 and PLS2) and principal components regression algorithms to obtain one optimised chemometric model for each algorithm. The spectral region selected for each model was between 1550 and 824 cm−1. The PLS1 model showed the best predictive capacity for simultaneous prediction of the six parameters. Excellent values of the coefficient of determination in validation (Rv2; 0.93–0.95) and residual predictive deviation (RPD; 4.28–6.95) were obtained for TPC, FC, ABTS and DPPH. Suitable values of Rv2 (0.91–0.92) and RPD (3.75–3.84) were obtained for TC and AC.