Home » MOL2NET » Section 11: SUIWML01: International Workshop on Machine Learning in Biomedicine, Soochow, 2016 » Paper

[] Machine-Learning models to predict the antioxidant capacity of food

1 Universidad de Camaguey Ignacio Agramonte Loynaz
2 Universidad de Santiago de Compostela
3 Universidad Estatal Amazónica
* Author to whom correspondence should be addressed.
12 December 2016
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The growing increase in the amount and type of nutrients in food created the necessity for a more efficient use in dietetics and nutrition. Flavonoids are exogenous dietary antioxidants and contribute to the total antioxidant capacity of the food. The current work aims to obtain optimal models to predict the total antioxidant properties of food by the ORAC method. A dataset based on the Database for the Flavonoid Content of Selected Foods and the Database for the Isoflavone Content of Selected Foods, was created. Different algorithms of artificial intelligence were applied, in particular Machine-Learning methods. They were employed using a R language. The performed study allowed to show the effectiveness of the models using structural-topologic features of Topological Substructural Molecular Design (TOPSMODE) in the databases. The proposed models can be considered, without overfitting, effective in predicting new values of ORAC, excepting the MultiLayer Perceptron (MLP) algorithm. The optimal model was obtained by the Random Forest (RF) algorithm, which presented the best R2 of the series (R2 = 0.9571313 for the training series and R2= 0.9247337 for the external prediction series).


Flavonoid, Total antioxidant capacity, Artificial intelligence, Machine-Learning methods, Random Forest algorithm.

Cite this article as

Guardado Yordi, E.; Koelig, R.; Matos, M.; Caballero Mota, Y.; Uriarte, E.; Pérez Martínez, A.; Santana, L.; Molina, E. Machine-Learning models to predict the antioxidant capacity of food. In Proceedings of the MOL2NET, International Conference on Multidisciplinary Sciences, 25 December 2016–25 January 2017; Sciforum Electronic Conference Series, Vol. 2, 2016 ; doi:10.3390/mol2net-02-03829


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