Unravelling effects of flavanols and their derivatives on acrylamide formation via support vector machine modelling

This study investigated the effect of flavanols and their derivatives on acrylamide formation under low-moisture conditions via prediction using the support vector regression (SVR) approach. Acrylamide was generated in a potato-based equimolar asparagine-reducing sugar model system through oven heating. Both positive and negative effects were observed when the flavonoid treatment ranged 1-10,000μmol/L. Flavanols and derivatives (100μmol/L) suppress the acrylamide formation within a range of 59.9-78.2%, while their maximal promotion effects ranged from 2.15-fold to 2.84-fold for the control at a concentration of 10,000μmol/L. The correlations between inhibition rates and changes in Trolox-equivalent antioxidant capacity (ΔTEAC) (R_TEAC-DPPH=0.878, R_TEAC-ABTS=0.882, R_TEAC-FRAP=0.871) were better than promotion rates (R_TEAC-DPPH=0.815, R_TEAC-ABTS=0.749, R_TEAC-FRAP=0.841). Using ΔTEAC as variables, an optimized SVR model could robustly serve as a new predictive tool for estimating the effect (R: 0.783-0.880), the fitting performance of which was slightly better than that of multiple linear regression model (R: 0.754-0.880).