Increasing interest in constituents and dietary supplements has created the need for more efficient use of this information in nutrition-related fields. The present work aims to obtain optimal models to predict the total antioxidant properties of food matrices, using available information on the amount and class of flavonoids present in vegetables. A new dataset using databases that collect the flavonoid content of selected foods has been created. Structural information was obtained using a structural-topological approach called TOPological Sub-Structural Molecular (TOPSMODE). Different artificial intelligence algorithms were applied, including Machine Learning (ML) methods. The study allowed us to demonstrate the effectiveness of the models using structural-topological characteristics of dietary flavonoids. The proposed models can be considered, without overfitting, effective in predicting new values of Oxygen Radical Absorption capacity (ORAC), except in the Multi-Layer Perceptron (MLP) algorithm. The best optimal model was obtained by the Random Forest (RF) algorithm. The in silico methodology we developed allows us to confirm the effectiveness of the obtained models, by introducing the new structural-topological attributes, as well as selecting those that most influence the class variable.

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).