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

Autores
Di Scala, Karina; Meschino, Gustavo; Vega-Gálvez, Antonio; Lemus-Mondaca, Roberto; Roura, Sara; 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.
Centro de Investigación y Desarrollo en Criotecnología de Alimentos
Facultad de Ingeniería
Materia
Ingeniería
Artificial neural networks
Dried apple
Genetic algorithm
Process optimization
Quality attributes
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc-sa/4.0/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/85211
Acceder
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oai_identifier_str oai:sedici.unlp.edu.ar:10915/85211
network_acronym_str SEDICI
repository_id_str 1329
network_name_str SEDICI (UNLP)
spelling An artificial neural network model for prediction of quality characteristics of apples during convective dehydrationDi Scala, KarinaMeschino, GustavoVega-Gálvez, AntonioLemus-Mondaca, RobertoRoura, SaraMascheroni, Rodolfo HoracioIngenieríaArtificial neural networksDried appleGenetic algorithmProcess optimizationQuality attributesIn 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.Centro de Investigación y Desarrollo en Criotecnología de AlimentosFacultad de Ingeniería2013info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionArticulohttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdf411-416http://sedici.unlp.edu.ar/handle/10915/85211enginfo:eu-repo/semantics/altIdentifier/issn/1678-457Xinfo:eu-repo/semantics/altIdentifier/doi/10.1590/S0101-20612013005000064info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/4.0/Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-09-29T11:16:30Zoai:sedici.unlp.edu.ar:10915/85211Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-29 11:16:30.929SEDICI (UNLP) - Universidad Nacional de La Platafalse
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
Ingeniería
Artificial neural networks
Dried apple
Genetic algorithm
Process optimization
Quality attributes
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
Meschino, Gustavo
Vega-Gálvez, Antonio
Lemus-Mondaca, Roberto
Roura, Sara
Mascheroni, Rodolfo Horacio
author Di Scala, Karina
author_facet Di Scala, Karina
Meschino, Gustavo
Vega-Gálvez, Antonio
Lemus-Mondaca, Roberto
Roura, Sara
Mascheroni, Rodolfo Horacio
author_role author
author2 Meschino, Gustavo
Vega-Gálvez, Antonio
Lemus-Mondaca, Roberto
Roura, Sara
Mascheroni, Rodolfo Horacio
author2_role author
author
author
author
author
dc.subject.none.fl_str_mv Ingeniería
Artificial neural networks
Dried apple
Genetic algorithm
Process optimization
Quality attributes
topic Ingeniería
Artificial neural networks
Dried apple
Genetic algorithm
Process optimization
Quality attributes
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.
Centro de Investigación y Desarrollo en Criotecnología de Alimentos
Facultad de Ingeniería
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
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Articulo
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://sedici.unlp.edu.ar/handle/10915/85211
url http://sedici.unlp.edu.ar/handle/10915/85211
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/issn/1678-457X
info:eu-repo/semantics/altIdentifier/doi/10.1590/S0101-20612013005000064
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc-sa/4.0/
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-sa/4.0/
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
dc.format.none.fl_str_mv application/pdf
411-416
dc.source.none.fl_str_mv reponame:SEDICI (UNLP)
instname:Universidad Nacional de La Plata
instacron:UNLP
reponame_str SEDICI (UNLP)
collection SEDICI (UNLP)
instname_str Universidad Nacional de La Plata
instacron_str UNLP
institution UNLP
repository.name.fl_str_mv SEDICI (UNLP) - Universidad Nacional de La Plata
repository.mail.fl_str_mv alira@sedici.unlp.edu.ar
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score 13.070432

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