Artificial intelligence applied to flavonoid data in food matrices
MetadataShow full item record
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. © 2019 by the authors
Showing items related by title, author, creator and subject.
A theoretical assessment of antioxidant capacity of flavonoids by means of local hyper–softness (2020) Sandoval-Yañez C.; Mascayano C.; Martínez-Araya J.I. (Elsevier B.V., 2018)
Evaluation of trypanocidal and antioxidant activities of a selected series of 3-amidocoumarins (2020) Moncada-Basualto M.; Lapier M.; Maya J.D.; Matsuhiro B.; Olea-Azar C.; Delogu G.L.; Uriarte E.; Santana L.; Matos M.J. (Bentham Science Publishers B.V., 2018)
Effect of thermal processing on the profile of bioactive compounds and antioxidant capacity of fermented orange juice (2020) Escudero-López B.; Cerrillo I.; Gil-Izquierdo Á.; Hornero-Méndez D.; Herrero-Martín G.; Berná G.; Medina S.; Ferreres F.; Martín F.; Fernández-Pachón M.-S. (Taylor and Francis Ltd, 2016)