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Open AccessArticle

Artificial Intelligence Applied to Flavonoid Data in Food Matrices

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Facultad de Ciencias Aplicadas, Universidad de Camagüey Ignacio Agramonte Loynaz, Cincunvalación Norte km 5 1/2, 74650 Camagüey, Cuba
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Facultad de Farmacia, Campus vida, Universidad de Santiago de Compostela, 15782 Santiago de Compostela, Spain
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CIQUP/Department of Chemistry and Biochemistry, Faculty of Sciences, University of Porto, 4169-007 Porto, Portugal
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Facultad de Ciencias de la Tierra, Universidad Estatal Amazónica, km 2 ½ vía Puyo a Tena (Paso Lateral), Puyo 032892-118, Ecuador
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Instituto de Ciencias Químicas Aplicadas, Universidad Autónoma de Chile, Santiago 7500912, Chile
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Author to whom correspondence should be addressed.
Foods 2019, 8(11), 573; https://doi.org/10.3390/foods8110573
Received: 7 October 2019 / Revised: 25 October 2019 / Accepted: 29 October 2019 / Published: 14 November 2019
(This article belongs to the Section Food Nutrition)
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. View Full-Text
Keywords: flavonoid; artificial intelligence; total antioxidant capacity flavonoid; artificial intelligence; total antioxidant capacity
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MDPI and ACS Style

Guardado Yordi, E.; Koelig, R.; Matos, M.J.; Pérez Martínez, A.; Caballero, Y.; Santana, L.; Pérez Quintana, M.; Molina, E.; Uriarte, E. Artificial Intelligence Applied to Flavonoid Data in Food Matrices. Foods 2019, 8, 573.

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