Prediction of the total antioxidant capacity of food based on artificial intelligence algorithms

The growing increase in the amount and type of nutrients in food created the necessity for a more efficient use applied to dietetics and nutrition. Flavonoids are exogenous dietetic antioxidants and contribute to the total antioxidant capacity of the food. This paper aims to explore the data using different algorithms of artificial intelligence to find the one that best predict the total antioxidant capacity of food by the oxygen radical absorbance capacity (ORAC) method. A record of composition data based on the Database for the Flavonoid Content of Selected Foods and the Database for the Isoflavone Content of Selected Foods, was created. The KNN (K-Nearest Neighbors) and supervised unidirectional networks MLP (MultiLayer Perceptron) technics were used. The attributes were: a) amount of flavonoid (mean), b) class of flavonoid, c) Trolox equivalent antioxidant capacity (TEAC) value of each flavonoid, d) probability of clastogenicity and clastogenicity classification by Quantitative Structure-Activity Relationship (QSAR) method and e) total polyphenol (TP) value. The variable to predict the activities was the ORAC value. For the prediction, a cross-validation method was used. For the KNN algorithm the optimal K value was 3, making clear the importance of the similarity between objects for the success of the results. It was concluded the successful use of the MLP and KNN techniques to predict the antioxidant capacity in the studied food groups.