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Application of KNN algorithm in determining the total antioxidant capacity of flavonoid-containing foods
* 1, 2 , 3 , 3 , 1 , 1 , 1 , 1, 2
1  Departamento de Química Orgánica, Facultad de Farmacia, Universidade de Santiago de Compostela, 15782 Santiago de Compostela, Spain
2  Universidad de Camagüey Ignacio Agramonte Loynaz, Facultad de Química, Cuba
3  Universidad de Camagüey Ignacio Agramonte Loynaz, Facultad de Informática, Cuba


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.

Keywords: Flavonoid, Artificial intelligence, MultiLayer Perceptron algorithm, K-Nearest Neighbors algorithm.