An artificial neural network model for prediction of quality characteristics of apples during convective dehydration

Detalhes bibliográficos
Autor(a) principal: Scala,Karina Di
Data de Publicação: 2013
Outros Autores: Meschino,Gustavo, Vega-gálvez,Antonio, Lemus-mondaca,Roberto, Roura,Sara, Mascheroni,Rodolfo
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Food Science and Technology (Campinas)
Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0101-20612013000300004
Resumo: In this study, the effects of hot-air drying conditions on color, water holding capacity, and total phenolic content of dried apple were investigated using artificial neural network as an intelligent modeling system. After that, a genetic algorithm was used to optimize the drying conditions. Apples were dried at different temperatures (40, 60, and 80 °C) and at three air flow-rates (0.5, 1, and 1.5 m/s). Applying the leave-one-out cross validation methodology, simulated and experimental data were in good agreement presenting an error < 2.4 %. Quality index optimal values were found at 62.9 °C and 1.0 m/s using genetic algorithm.
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spelling An artificial neural network model for prediction of quality characteristics of apples during convective dehydrationartificial neural networksquality attributesgenetic algorithmprocess optimizationdried appleIn this study, the effects of hot-air drying conditions on color, water holding capacity, and total phenolic content of dried apple were investigated using artificial neural network as an intelligent modeling system. After that, a genetic algorithm was used to optimize the drying conditions. Apples were dried at different temperatures (40, 60, and 80 °C) and at three air flow-rates (0.5, 1, and 1.5 m/s). Applying the leave-one-out cross validation methodology, simulated and experimental data were in good agreement presenting an error < 2.4 %. Quality index optimal values were found at 62.9 °C and 1.0 m/s using genetic algorithm.Sociedade Brasileira de Ciência e Tecnologia de Alimentos2013-09-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0101-20612013000300004Food Science and Technology v.33 n.3 2013reponame:Food Science and Technology (Campinas)instname:Sociedade Brasileira de Ciência e Tecnologia de Alimentos (SBCTA)instacron:SBCTA10.1590/S0101-20612013005000064info:eu-repo/semantics/openAccessScala,Karina DiMeschino,GustavoVega-gálvez,AntonioLemus-mondaca,RobertoRoura,SaraMascheroni,Rodolfoeng2013-10-08T00:00:00Zoai:scielo:S0101-20612013000300004Revistahttp://www.scielo.br/ctaONGhttps://old.scielo.br/oai/scielo-oai.php||revista@sbcta.org.br1678-457X0101-2061opendoar:2013-10-08T00:00Food Science and Technology (Campinas) - Sociedade Brasileira de Ciência e Tecnologia de Alimentos (SBCTA)false
dc.title.none.fl_str_mv An artificial neural network model for prediction of quality characteristics of apples during convective dehydration
title An artificial neural network model for prediction of quality characteristics of apples during convective dehydration
spellingShingle An artificial neural network model for prediction of quality characteristics of apples during convective dehydration
Scala,Karina Di
artificial neural networks
quality attributes
genetic algorithm
process optimization
dried apple
title_short An artificial neural network model for prediction of quality characteristics of apples during convective dehydration
title_full An artificial neural network model for prediction of quality characteristics of apples during convective dehydration
title_fullStr An artificial neural network model for prediction of quality characteristics of apples during convective dehydration
title_full_unstemmed An artificial neural network model for prediction of quality characteristics of apples during convective dehydration
title_sort An artificial neural network model for prediction of quality characteristics of apples during convective dehydration
author Scala,Karina Di
author_facet Scala,Karina Di
Meschino,Gustavo
Vega-gálvez,Antonio
Lemus-mondaca,Roberto
Roura,Sara
Mascheroni,Rodolfo
author_role author
author2 Meschino,Gustavo
Vega-gálvez,Antonio
Lemus-mondaca,Roberto
Roura,Sara
Mascheroni,Rodolfo
author2_role author
author
author
author
author
dc.contributor.author.fl_str_mv Scala,Karina Di
Meschino,Gustavo
Vega-gálvez,Antonio
Lemus-mondaca,Roberto
Roura,Sara
Mascheroni,Rodolfo
dc.subject.por.fl_str_mv artificial neural networks
quality attributes
genetic algorithm
process optimization
dried apple
topic artificial neural networks
quality attributes
genetic algorithm
process optimization
dried apple
description In this study, the effects of hot-air drying conditions on color, water holding capacity, and total phenolic content of dried apple were investigated using artificial neural network as an intelligent modeling system. After that, a genetic algorithm was used to optimize the drying conditions. Apples were dried at different temperatures (40, 60, and 80 °C) and at three air flow-rates (0.5, 1, and 1.5 m/s). Applying the leave-one-out cross validation methodology, simulated and experimental data were in good agreement presenting an error < 2.4 %. Quality index optimal values were found at 62.9 °C and 1.0 m/s using genetic algorithm.
publishDate 2013
dc.date.none.fl_str_mv 2013-09-01
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0101-20612013000300004
url http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0101-20612013000300004
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 10.1590/S0101-20612013005000064
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv text/html
dc.publisher.none.fl_str_mv Sociedade Brasileira de Ciência e Tecnologia de Alimentos
publisher.none.fl_str_mv Sociedade Brasileira de Ciência e Tecnologia de Alimentos
dc.source.none.fl_str_mv Food Science and Technology v.33 n.3 2013
reponame:Food Science and Technology (Campinas)
instname:Sociedade Brasileira de Ciência e Tecnologia de Alimentos (SBCTA)
instacron:SBCTA
instname_str Sociedade Brasileira de Ciência e Tecnologia de Alimentos (SBCTA)
instacron_str SBCTA
institution SBCTA
reponame_str Food Science and Technology (Campinas)
collection Food Science and Technology (Campinas)
repository.name.fl_str_mv Food Science and Technology (Campinas) - Sociedade Brasileira de Ciência e Tecnologia de Alimentos (SBCTA)
repository.mail.fl_str_mv ||revista@sbcta.org.br
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