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

Autores
Di Scala, Karina Cecilia; Meschino, Gustavo; Vega Gálvez, Antonio; Lemus Mondaca, Roberto; Roura, Sara Ines; Mascheroni, Rodolfo Horacio
Año de publicación
2013
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
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.
Fil: Di Scala, Karina Cecilia. Universidad Nacional de Mar del Plata. Facultad de Ingenieria. Departamento de Ingenieria Quimica. Grupo de Inv En Ingenieria En Alimentos; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Meschino, Gustavo. Universidad Nacional de Mar del Plata. Facultad de Ingenieria. Departamento de Ingenieria Electronica. Laboratorio de Bioingenieria; Argentina
Fil: Vega Gálvez, Antonio. Universidad de la Serena; Chile
Fil: Lemus Mondaca, Roberto. Universidad de la Serena; Chile
Fil: Roura, Sara Ines. Universidad Nacional de Mar del Plata. Facultad de Ingenieria. Departamento de Ingenieria Quimica. Grupo de Inv En Ingenieria En Alimentos; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Mascheroni, Rodolfo Horacio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico la Plata. Centro de Investigaciones en Criotecnología de Alimentos (i); Argentina. Universidad Nacional de La Plata; Argentina
Materia
Artificial neural networks
Quality attributes
Genetic algorithm
Process optimization
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/14481
Acceder
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network_name_str CONICET Digital (CONICET)
spelling An artificial neural network model for prediction of quality characteristics of apples during convective dehydrationDi Scala, Karina CeciliaMeschino, GustavoVega Gálvez, AntonioLemus Mondaca, RobertoRoura, Sara InesMascheroni, Rodolfo HoracioArtificial neural networksQuality attributesGenetic algorithmProcess optimizationhttps://purl.org/becyt/ford/2.11https://purl.org/becyt/ford/2In 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.Fil: Di Scala, Karina Cecilia. Universidad Nacional de Mar del Plata. Facultad de Ingenieria. Departamento de Ingenieria Quimica. Grupo de Inv En Ingenieria En Alimentos; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Meschino, Gustavo. Universidad Nacional de Mar del Plata. Facultad de Ingenieria. Departamento de Ingenieria Electronica. Laboratorio de Bioingenieria; ArgentinaFil: Vega Gálvez, Antonio. Universidad de la Serena; ChileFil: Lemus Mondaca, Roberto. Universidad de la Serena; ChileFil: Roura, Sara Ines. Universidad Nacional de Mar del Plata. Facultad de Ingenieria. Departamento de Ingenieria Quimica. Grupo de Inv En Ingenieria En Alimentos; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Mascheroni, Rodolfo Horacio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico la Plata. Centro de Investigaciones en Criotecnología de Alimentos (i); Argentina. Universidad Nacional de La Plata; ArgentinaSoc Brasileira Ciencia Tecnologia Alimentos2013-08info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/14481Di Scala, Karina Cecilia; Meschino, Gustavo; Vega Gálvez, Antonio; Lemus Mondaca, Roberto; Roura, Sara Ines; et al.; An artificial neural network model for prediction of quality characteristics of apples during convective dehydration; Soc Brasileira Ciencia Tecnologia Alimentos; Ciencia e Tecnologia de Alimentos; 33; 3; 8-2013; 411-4160101-20611678-457Xenginfo:eu-repo/semantics/altIdentifier/doi/10.1590/S0101-20612013005000064info:eu-repo/semantics/altIdentifier/url/http://ref.scielo.org/8v6jfrinfo:eu-repo/semantics/altIdentifier/url/http://www.redalyc.org/articulo.oa?id=395940117004info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T09:54:35Zoai:ri.conicet.gov.ar:11336/14481instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-29 09:54:35.344CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
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
Di Scala, Karina Cecilia
Artificial neural networks
Quality attributes
Genetic algorithm
Process optimization
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
dc.creator.none.fl_str_mv Di Scala, Karina Cecilia
Meschino, Gustavo
Vega Gálvez, Antonio
Lemus Mondaca, Roberto
Roura, Sara Ines
Mascheroni, Rodolfo Horacio
author Di Scala, Karina Cecilia
author_facet Di Scala, Karina Cecilia
Meschino, Gustavo
Vega Gálvez, Antonio
Lemus Mondaca, Roberto
Roura, Sara Ines
Mascheroni, Rodolfo Horacio
author_role author
author2 Meschino, Gustavo
Vega Gálvez, Antonio
Lemus Mondaca, Roberto
Roura, Sara Ines
Mascheroni, Rodolfo Horacio
author2_role author
author
author
author
author
dc.subject.none.fl_str_mv Artificial neural networks
Quality attributes
Genetic algorithm
Process optimization
topic Artificial neural networks
Quality attributes
Genetic algorithm
Process optimization
purl_subject.fl_str_mv https://purl.org/becyt/ford/2.11
https://purl.org/becyt/ford/2
dc.description.none.fl_txt_mv 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.
Fil: Di Scala, Karina Cecilia. Universidad Nacional de Mar del Plata. Facultad de Ingenieria. Departamento de Ingenieria Quimica. Grupo de Inv En Ingenieria En Alimentos; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Meschino, Gustavo. Universidad Nacional de Mar del Plata. Facultad de Ingenieria. Departamento de Ingenieria Electronica. Laboratorio de Bioingenieria; Argentina
Fil: Vega Gálvez, Antonio. Universidad de la Serena; Chile
Fil: Lemus Mondaca, Roberto. Universidad de la Serena; Chile
Fil: Roura, Sara Ines. Universidad Nacional de Mar del Plata. Facultad de Ingenieria. Departamento de Ingenieria Quimica. Grupo de Inv En Ingenieria En Alimentos; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Mascheroni, Rodolfo Horacio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico la Plata. Centro de Investigaciones en Criotecnología de Alimentos (i); Argentina. Universidad Nacional de La Plata; Argentina
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 2013-08
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/14481
Di Scala, Karina Cecilia; Meschino, Gustavo; Vega Gálvez, Antonio; Lemus Mondaca, Roberto; Roura, Sara Ines; et al.; An artificial neural network model for prediction of quality characteristics of apples during convective dehydration; Soc Brasileira Ciencia Tecnologia Alimentos; Ciencia e Tecnologia de Alimentos; 33; 3; 8-2013; 411-416
0101-2061
1678-457X
url http://hdl.handle.net/11336/14481
identifier_str_mv Di Scala, Karina Cecilia; Meschino, Gustavo; Vega Gálvez, Antonio; Lemus Mondaca, Roberto; Roura, Sara Ines; et al.; An artificial neural network model for prediction of quality characteristics of apples during convective dehydration; Soc Brasileira Ciencia Tecnologia Alimentos; Ciencia e Tecnologia de Alimentos; 33; 3; 8-2013; 411-416
0101-2061
1678-457X
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1590/S0101-20612013005000064
info:eu-repo/semantics/altIdentifier/url/http://ref.scielo.org/8v6jfr
info:eu-repo/semantics/altIdentifier/url/http://www.redalyc.org/articulo.oa?id=395940117004
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Soc Brasileira Ciencia Tecnologia Alimentos
publisher.none.fl_str_mv Soc Brasileira Ciencia Tecnologia Alimentos
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 13.070432

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