Artificial neural network modelling of the antioxidant activity and phenolic compounds of bananas submitted to different drying treatments
Autor(a) principal: | |
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Data de Publicação: | 2015 |
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/10316/27833 https://doi.org/10.1016/j.foodchem.2014.07.094 |
Resumo: | Bananas (cv. Musa nana and Musa cavendishii) fresh and dried by hot air at 50 and 70 °C and lyophilisation were analysed for phenolic contents and antioxidant activity. All samples were subject to six extractions (three with methanol followed by three with acetone/water solution). The experimental data served to train a neural network adequate to describe the experimental observations for both output variables studied: total phenols and antioxidant activity. The results show that both bananas are similar and air drying decreased total phenols and antioxidant activity for both temperatures, whereas lyophilisation decreased the phenolic content in a lesser extent. Neural network experiments showed that antioxidant activity and phenolic compounds can be predicted accurately from the input variables: banana variety, dryness state and type and order of extract. Drying state and extract order were found to have larger impact in the values of antioxidant activity and phenolic compounds. |
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Artificial neural network modelling of the antioxidant activity and phenolic compounds of bananas submitted to different drying treatmentsAntioxidant activityBananaDryingNeural networkPhenolic compoundsBananas (cv. Musa nana and Musa cavendishii) fresh and dried by hot air at 50 and 70 °C and lyophilisation were analysed for phenolic contents and antioxidant activity. All samples were subject to six extractions (three with methanol followed by three with acetone/water solution). The experimental data served to train a neural network adequate to describe the experimental observations for both output variables studied: total phenols and antioxidant activity. The results show that both bananas are similar and air drying decreased total phenols and antioxidant activity for both temperatures, whereas lyophilisation decreased the phenolic content in a lesser extent. Neural network experiments showed that antioxidant activity and phenolic compounds can be predicted accurately from the input variables: banana variety, dryness state and type and order of extract. Drying state and extract order were found to have larger impact in the values of antioxidant activity and phenolic compounds.Elsevier2015-02-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://hdl.handle.net/10316/27833http://hdl.handle.net/10316/27833https://doi.org/10.1016/j.foodchem.2014.07.094engGUINÉ, Raquel P. F. [et. al] - Artificial neural network modelling of the antioxidant activity and phenolic compounds of bananas submitted to different drying treatments. "Food Chemistry". ISSN 0308-8146. Vol. 168 (2014) p. 454–4590308-8146http://www.sciencedirect.com/science/article/pii/S030881461401142XGuiné, Raquel P. F.Barroca, Maria JoãoGonçalves, Fernando J.Alves, MarianaOliveira, SolangeMendes, Mateusinfo: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:RCAAP2020-05-29T09:42:30Zoai:estudogeral.uc.pt:10316/27833Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T20:53:46.612936Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Artificial neural network modelling of the antioxidant activity and phenolic compounds of bananas submitted to different drying treatments |
title |
Artificial neural network modelling of the antioxidant activity and phenolic compounds of bananas submitted to different drying treatments |
spellingShingle |
Artificial neural network modelling of the antioxidant activity and phenolic compounds of bananas submitted to different drying treatments Guiné, Raquel P. F. Antioxidant activity Banana Drying Neural network Phenolic compounds |
title_short |
Artificial neural network modelling of the antioxidant activity and phenolic compounds of bananas submitted to different drying treatments |
title_full |
Artificial neural network modelling of the antioxidant activity and phenolic compounds of bananas submitted to different drying treatments |
title_fullStr |
Artificial neural network modelling of the antioxidant activity and phenolic compounds of bananas submitted to different drying treatments |
title_full_unstemmed |
Artificial neural network modelling of the antioxidant activity and phenolic compounds of bananas submitted to different drying treatments |
title_sort |
Artificial neural network modelling of the antioxidant activity and phenolic compounds of bananas submitted to different drying treatments |
author |
Guiné, Raquel P. F. |
author_facet |
Guiné, Raquel P. F. Barroca, Maria João Gonçalves, Fernando J. Alves, Mariana Oliveira, Solange Mendes, Mateus |
author_role |
author |
author2 |
Barroca, Maria João Gonçalves, Fernando J. Alves, Mariana Oliveira, Solange Mendes, Mateus |
author2_role |
author author author author author |
dc.contributor.author.fl_str_mv |
Guiné, Raquel P. F. Barroca, Maria João Gonçalves, Fernando J. Alves, Mariana Oliveira, Solange Mendes, Mateus |
dc.subject.por.fl_str_mv |
Antioxidant activity Banana Drying Neural network Phenolic compounds |
topic |
Antioxidant activity Banana Drying Neural network Phenolic compounds |
description |
Bananas (cv. Musa nana and Musa cavendishii) fresh and dried by hot air at 50 and 70 °C and lyophilisation were analysed for phenolic contents and antioxidant activity. All samples were subject to six extractions (three with methanol followed by three with acetone/water solution). The experimental data served to train a neural network adequate to describe the experimental observations for both output variables studied: total phenols and antioxidant activity. The results show that both bananas are similar and air drying decreased total phenols and antioxidant activity for both temperatures, whereas lyophilisation decreased the phenolic content in a lesser extent. Neural network experiments showed that antioxidant activity and phenolic compounds can be predicted accurately from the input variables: banana variety, dryness state and type and order of extract. Drying state and extract order were found to have larger impact in the values of antioxidant activity and phenolic compounds. |
publishDate |
2015 |
dc.date.none.fl_str_mv |
2015-02-01 |
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/10316/27833 http://hdl.handle.net/10316/27833 https://doi.org/10.1016/j.foodchem.2014.07.094 |
url |
http://hdl.handle.net/10316/27833 https://doi.org/10.1016/j.foodchem.2014.07.094 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
GUINÉ, Raquel P. F. [et. al] - Artificial neural network modelling of the antioxidant activity and phenolic compounds of bananas submitted to different drying treatments. "Food Chemistry". ISSN 0308-8146. Vol. 168 (2014) p. 454–459 0308-8146 http://www.sciencedirect.com/science/article/pii/S030881461401142X |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.publisher.none.fl_str_mv |
Elsevier |
publisher.none.fl_str_mv |
Elsevier |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
repository.mail.fl_str_mv |
|
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1799133823802277888 |