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Artificial intelligence applied to flavonoid data in food matrices

dc.contributor.authorYordi E.G.
dc.contributor.authorKoelig R.
dc.contributor.authorMatos M.J.
dc.contributor.authorMartínez A.P.
dc.contributor.authorCaballero Y.
dc.contributor.authorSantana L.
dc.contributor.authorQuintana M.P.
dc.contributor.authorMolina E.
dc.contributor.authorUriarte E.
dc.date.accessioned2020-09-02T22:30:37Z
dc.date.available2020-09-02T22:30:37Z
dc.date.issued2019
dc.identifier10.3390/foods8110573
dc.identifier.citation8, 11, -
dc.identifier.issn23048158
dc.identifier.urihttps://hdl.handle.net/20.500.12728/6651
dc.descriptionIncreasing 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
dc.language.isoen
dc.publisherMDPI Multidisciplinary Digital Publishing Institute
dc.subjectArtificial intelligence
dc.subjectFlavonoid
dc.subjectTotal antioxidant capacity
dc.titleArtificial intelligence applied to flavonoid data in food matrices
dc.typeArticle


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