Artificial neural networks for prediction of antioxidant capacity of cruciferous sprouts

The aim of this work was to show that artificial neural networks (ANNs) are the convenient tool for modeling the biological properties of food. For this reason, as a good example, known contents of bioactive compounds of cruciferous sprouts were taken for the prediction of their trolox equivalent antioxidant capacity–one of the most important biological properties of food. The input data reflected the contents of the following compounds in cruciferous seeds in the course of germination: total phenolic compounds (TPC), inositol hexaphosphate (InsP₆), glucosinolates (GLS), soluble proteins (SP), ascorbic acid (AH₂), and total tocopherols (T_tot). Additionally, the trolox equivalent antioxidant capacity of germinated cruciferous seeds (TEAC_exp) was determined. The used ANN which was trained on the learning set, generalized the obtained prediction ability in respect to the data contained in validating and testing sets. The predicted TAEC values calculated by ANN were satisfactory correlated with experimental TAEC values. Therefore, the ANN seems to find application in the quality analysis of functional properties of food of plant origin for the prediction of the trolox equivalent antioxidant capacity.