Flavonoids are bioactive compounds that can display antioxidant activity. Their must important source is the vegetal kingdom. Their composition in different foods is compiled into several databases organized by USDA. This information enabled the creation of a data record that was used in the work to predict the total antioxidant capacity of food by the oxygen radical absorbance capacity (ORAC) method, using algorithms of artificial intelligence. K-Nearest Neighbors (KNN) was used. The attributes were: a) amount of flavonoid, b) class of flavonoid, c) Trolox equivalent antioxidant capacity (TEAC) value, d) probability of clastogenicity and clastogenicity classification by Quantitative Structure-Activity Relationship (QSAR) method and e) total polyphenol (TP) value. The selected variable to predict 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 KNN algorithm to predict the antioxidant capacity in the studied food groups.
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Application of KNN algorithm in determining the total antioxidant capacity of flavonoid-containing foods
Published: 30 October 2015 by MDPI in The 19th International Electronic Conference on Synthetic Organic Chemistry session Computational Chemistry
Keywords: Flavonoid, Artificial intelligence, MultiLayer Perceptron algorithm, K-Nearest Neighbors algorithm.