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Extraction of Phenolic Compounds with Antioxidant Activity from Strawberries: Modelling with Artificial Neural Networks (ANNs)

2020Journal article Open access
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dc.contributor.authorGolpour, I.
dc.contributor.authorFerrão, A. C.
dc.contributor.authorGonçalves, F.
dc.contributor.authorCorreia, Paula
dc.contributor.authorBlanco-Marigorta, A. M.
dc.contributor.authorGuiné, Raquel P. F.
dc.date.accessioned2021-09-22T13:01:55Z
dc.date.available2021-09-22T13:01:55Z
dc.date.issued2020
dc.description.abstracthis research study focuses on the evaluation of the total phenolic compounds (TPC) and antioxidant activity (AOA) of strawberries according to different experimental extraction conditions by applying the Artificial Neural Networks (ANNs) technique. The experimental data were applied to train ANNs using feed- and cascade-forward backpropagation models with Levenberg-Marquardt (LM) and Bayesian Regulation (BR) algorithms. Three independent variables (solvent concentration, volume/mass ratio and extraction time) were used as ANN inputs, whereas the three variables of total phenolic compounds, DPPH and ABTS antioxidant activities were considered as ANN outputs. The results demonstrate that the best cascade- and feed-forward backpropagation topologies of ANNs for the prediction of total phenolic compounds and DPPH and ABTS antioxidant activity factors were the 3-9-1, 3-4-4-1 and 3-13-10-1 structures, with the training algorithms of trainlm, trainbr, trainlm and threshold functions of tansig-purelin, tansig-tansig-tansig and purelin-tansig-tansig, respectively. The best R2 values for the predication of total phenolic compounds and DPPH and ABTS antioxidant activity factors were 0.9806 (MSE = 0.0047), 0.9651 (MSE = 0.0035) and 0.9756 (MSE = 0.00286), respectively. According to the comparison of ANNs, the results showed that the cascade-forward backpropagation network showed better performance than the feed-forward backpropagation network for predicting the TPC, and the FFBP network, in predicting the DPPH and ABTS antioxidant activity factors, had more precision than the cascade-forward backpropagation network. The ANN technique is a potential method for estimating targeted total phenolic compounds and the antioxidant activity of strawberries.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationGolpour I, Ferrão AC, Gonçalves F, Correia PMR, Blanco-Marigorta AM, Guiné RPF. (2021) Extraction of Phenolic Compounds with Antioxidant Activity from Strawberries: Modelling with Artificial Neural Networks (ANNs). Foods, 10(9), 2228:1-13.pt_PT
dc.identifier.urihttp://hdl.handle.net/10400.19/6840
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.subjectstrawberriespt_PT
dc.subjecttotal phenolic compoundspt_PT
dc.subjectantioxidant activitypt_PT
dc.subjectartificial neural networkspt_PT
dc.titleExtraction of Phenolic Compounds with Antioxidant Activity from Strawberries: Modelling with Artificial Neural Networks (ANNs)pt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.citation.issue9pt_PT
oaire.citation.startPage2228pt_PT
oaire.citation.titleFoodspt_PT
oaire.citation.volume10pt_PT
person.familyNameCorreia
person.familyNamede Pinho Ferreira Guiné
person.givenNamePaula
person.givenNameRaquel
person.identifierhttps://scholar.google.pt/citations?user=abFDovIAAAAJ&hl=pt-PT
person.identifier.ciencia-id7915-FB81-4520
person.identifier.ciencia-id8B13-5492-0F23
person.identifier.orcid0000-0002-2023-4475
person.identifier.orcid0000-0003-0595-6805
person.identifier.scopus-author-id24597116100
person.identifier.scopus-author-id6603138390
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
relation.isAuthorOfPublication9395b4b0-ffd1-4f2d-a99c-4bb5cac701c0
relation.isAuthorOfPublication59580952-77cc-4e4e-ae90-527a8b994f9f
relation.isAuthorOfPublication.latestForDiscovery9395b4b0-ffd1-4f2d-a99c-4bb5cac701c0

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