Modelling Through Artificial Neural Networks of the Phenolic Compounds and Antioxidant Activity of Blueberries
Autor(a) principal: | |
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Data de Publicação: | 2018 |
Outros Autores: | , , , , |
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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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 |
format |
article |
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 |
dc.source.none.fl_str_mv |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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RCAAP |
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RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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repository.mail.fl_str_mv |
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