Modelling Through Artificial Neural Networks of the Phenolic Compounds and Antioxidant Activity of Blueberries

Detalhes bibliográficos
Autor(a) principal: Guiné, Raquel
Data de Publicação: 2018
Outros Autores: Gonçalves, Christophe, Matos, Susana, Gonçalves, Fernando, Costa, Daniela Vasconcelos Teixeira da, Mendes, Mateus
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10400.19/5275
Resumo: The present study aimed at investigating the influence of several production factors, conservation conditions, and extraction procedures on the phenolic compounds and antioxidant activity of blueberries from different cultivars. The experimental data was used to train artificial neural networks, using a feed-forward model, which gave information about the variables affecting the antioxidant activity and the concentration of phenolic compounds in blueberries. The ANN input variables were location, cultivar, the age of the bushes, the altitude of the farm, production mode, state, storage time, type of extract and order of extract, while the output variables were total phenolic compounds, tannins as well as ABTS and DPPH antioxidant activity. The ANN model was fairly good, with values of the correlation factor for the whole dataset varying from 0.948 to 0.979, while the values of mean squared error were ranging from 0.846 to 0.018, for DPPH antioxidant acidity and anthocyanins, respectively. The results obtained showed that the methanol extracts contained higher amounts of total phenols and anthocyanins as compared to acetone: water extracts, while presenting similar quantities of tannins in both extracts. The blueberries from organic farming were richer in phenolic compounds and possessed higher antioxidant activity than those from conventional agriculture. Even though the effect of storage was not established with high certainty, a trend was observed for an increase in the phenolic compounds and antioxidant activity along storage, either when under refrigeration or under freezing, for the storage periods evaluated.
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spelling Modelling Through Artificial Neural Networks of the Phenolic Compounds and Antioxidant Activity of BlueberriesAntioxidant activityArtificial neural networkPhenolic compoundsThe present study aimed at investigating the influence of several production factors, conservation conditions, and extraction procedures on the phenolic compounds and antioxidant activity of blueberries from different cultivars. The experimental data was used to train artificial neural networks, using a feed-forward model, which gave information about the variables affecting the antioxidant activity and the concentration of phenolic compounds in blueberries. The ANN input variables were location, cultivar, the age of the bushes, the altitude of the farm, production mode, state, storage time, type of extract and order of extract, while the output variables were total phenolic compounds, tannins as well as ABTS and DPPH antioxidant activity. The ANN model was fairly good, with values of the correlation factor for the whole dataset varying from 0.948 to 0.979, while the values of mean squared error were ranging from 0.846 to 0.018, for DPPH antioxidant acidity and anthocyanins, respectively. The results obtained showed that the methanol extracts contained higher amounts of total phenols and anthocyanins as compared to acetone: water extracts, while presenting similar quantities of tannins in both extracts. The blueberries from organic farming were richer in phenolic compounds and possessed higher antioxidant activity than those from conventional agriculture. Even though the effect of storage was not established with high certainty, a trend was observed for an increase in the phenolic compounds and antioxidant activity along storage, either when under refrigeration or under freezing, for the storage periods evaluated.Repositório Científico do Instituto Politécnico de ViseuGuiné, RaquelGonçalves, ChristopheMatos, SusanaGonçalves, FernandoCosta, Daniela Vasconcelos Teixeira daMendes, Mateus2018-11-08T16:34:41Z20182018-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.19/5275engGuiné, R.P.F., Gonçalves, C., Matos, S., Gonçalves, F., Costa, D.V.T.A. & Mendes, M. (2018). Modelling Through Artificial Neural Networks of the Phenolic Compounds and Antioxidant Activity of Blueberries. Iranian Journal of Chemistry and Chemical Engineering, 37(2), 193-212. Retrieved from http://www.ijcce.ac.ir/article_30699_cb7998101a3d57b514c2fc014f022595.pdfinfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-01-16T15:27:57ZPortal AgregadorONG
dc.title.none.fl_str_mv Modelling Through Artificial Neural Networks of the Phenolic Compounds and Antioxidant Activity of Blueberries
title Modelling Through Artificial Neural Networks of the Phenolic Compounds and Antioxidant Activity of Blueberries
spellingShingle Modelling Through Artificial Neural Networks of the Phenolic Compounds and Antioxidant Activity of Blueberries
Guiné, Raquel
Antioxidant activity
Artificial neural network
Phenolic compounds
title_short Modelling Through Artificial Neural Networks of the Phenolic Compounds and Antioxidant Activity of Blueberries
title_full Modelling Through Artificial Neural Networks of the Phenolic Compounds and Antioxidant Activity of Blueberries
title_fullStr Modelling Through Artificial Neural Networks of the Phenolic Compounds and Antioxidant Activity of Blueberries
title_full_unstemmed Modelling Through Artificial Neural Networks of the Phenolic Compounds and Antioxidant Activity of Blueberries
title_sort Modelling Through Artificial Neural Networks of the Phenolic Compounds and Antioxidant Activity of Blueberries
author Guiné, Raquel
author_facet Guiné, Raquel
Gonçalves, Christophe
Matos, Susana
Gonçalves, Fernando
Costa, Daniela Vasconcelos Teixeira da
Mendes, Mateus
author_role author
author2 Gonçalves, Christophe
Matos, Susana
Gonçalves, Fernando
Costa, Daniela Vasconcelos Teixeira da
Mendes, Mateus
author2_role author
author
author
author
author
dc.contributor.none.fl_str_mv Repositório Científico do Instituto Politécnico de Viseu
dc.contributor.author.fl_str_mv Guiné, Raquel
Gonçalves, Christophe
Matos, Susana
Gonçalves, Fernando
Costa, Daniela Vasconcelos Teixeira da
Mendes, Mateus
dc.subject.por.fl_str_mv Antioxidant activity
Artificial neural network
Phenolic compounds
topic Antioxidant activity
Artificial neural network
Phenolic compounds
description The present study aimed at investigating the influence of several production factors, conservation conditions, and extraction procedures on the phenolic compounds and antioxidant activity of blueberries from different cultivars. The experimental data was used to train artificial neural networks, using a feed-forward model, which gave information about the variables affecting the antioxidant activity and the concentration of phenolic compounds in blueberries. The ANN input variables were location, cultivar, the age of the bushes, the altitude of the farm, production mode, state, storage time, type of extract and order of extract, while the output variables were total phenolic compounds, tannins as well as ABTS and DPPH antioxidant activity. The ANN model was fairly good, with values of the correlation factor for the whole dataset varying from 0.948 to 0.979, while the values of mean squared error were ranging from 0.846 to 0.018, for DPPH antioxidant acidity and anthocyanins, respectively. The results obtained showed that the methanol extracts contained higher amounts of total phenols and anthocyanins as compared to acetone: water extracts, while presenting similar quantities of tannins in both extracts. The blueberries from organic farming were richer in phenolic compounds and possessed higher antioxidant activity than those from conventional agriculture. Even though the effect of storage was not established with high certainty, a trend was observed for an increase in the phenolic compounds and antioxidant activity along storage, either when under refrigeration or under freezing, for the storage periods evaluated.
publishDate 2018
dc.date.none.fl_str_mv 2018-11-08T16:34:41Z
2018
2018-01-01T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
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status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10400.19/5275
url http://hdl.handle.net/10400.19/5275
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Guiné, R.P.F., Gonçalves, C., Matos, S., Gonçalves, F., Costa, D.V.T.A. & Mendes, M. (2018). Modelling Through Artificial Neural Networks of the Phenolic Compounds and Antioxidant Activity of Blueberries. Iranian Journal of Chemistry and Chemical Engineering, 37(2), 193-212. Retrieved from http://www.ijcce.ac.ir/article_30699_cb7998101a3d57b514c2fc014f022595.pdf
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
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