Modelling of mass transfer kinetic in osmotic dehydration of kiwifruit
Autor(a) principal: | |
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Data de Publicação: | 2016 |
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/10400.1/9528 |
Resumo: | Osmotic dehydration characteristics of kiwifruit were predicted by different activation functions of an artificial neural network. Osmotic solution concentration (y(1)), osmotic solution temperature (y(2)), and immersion time (y(3)) were considered as the input parameters and solid gain value (x(1)) and water loss value (x(2)) were selected as the outlet parameters of the network. The result showed that logarithm sigmoid activation function has greater performance than tangent hyperbolic activation function for the prediction of osmotic dehydration parameters of kiwifruit. The minimum mean relative error for the solid gain and water loss parameters with one hidden layer and 19 nods were 0.00574 and 0.0062% for logarithm sigmoid activation function, respectively, which introduced logarithm sigmoid function as a more appropriate tool in the prediction of the osmotic dehydration of kiwifruit slices. As a result, it is concluded that this network is capable in the prediction of solid gain and water loss parameters (responses) with the correlation coefficient values of 0.986 and 0.989, respectively. |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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7160 |
spelling |
Modelling of mass transfer kinetic in osmotic dehydration of kiwifruitOsmotic dehydration characteristics of kiwifruit were predicted by different activation functions of an artificial neural network. Osmotic solution concentration (y(1)), osmotic solution temperature (y(2)), and immersion time (y(3)) were considered as the input parameters and solid gain value (x(1)) and water loss value (x(2)) were selected as the outlet parameters of the network. The result showed that logarithm sigmoid activation function has greater performance than tangent hyperbolic activation function for the prediction of osmotic dehydration parameters of kiwifruit. The minimum mean relative error for the solid gain and water loss parameters with one hidden layer and 19 nods were 0.00574 and 0.0062% for logarithm sigmoid activation function, respectively, which introduced logarithm sigmoid function as a more appropriate tool in the prediction of the osmotic dehydration of kiwifruit slices. As a result, it is concluded that this network is capable in the prediction of solid gain and water loss parameters (responses) with the correlation coefficient values of 0.986 and 0.989, respectively.SapientiaJabrayili, SharokhFarzaneh, VahidZare, ZahraBakhshabadi, HamidBabazadeh, ZahraMokhtarian, MohsenCarvalho, Isabel Saraiva de2017-04-07T15:56:48Z2016-042016-04-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.1/9528eng0236-8722AUT: ICA01121;10.1515/intag-2015-0091info: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:RCAAP2023-07-24T10:21:00Zoai:sapientia.ualg.pt:10400.1/9528Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T20:01:27.135598Repositório 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 |
Modelling of mass transfer kinetic in osmotic dehydration of kiwifruit |
title |
Modelling of mass transfer kinetic in osmotic dehydration of kiwifruit |
spellingShingle |
Modelling of mass transfer kinetic in osmotic dehydration of kiwifruit Jabrayili, Sharokh |
title_short |
Modelling of mass transfer kinetic in osmotic dehydration of kiwifruit |
title_full |
Modelling of mass transfer kinetic in osmotic dehydration of kiwifruit |
title_fullStr |
Modelling of mass transfer kinetic in osmotic dehydration of kiwifruit |
title_full_unstemmed |
Modelling of mass transfer kinetic in osmotic dehydration of kiwifruit |
title_sort |
Modelling of mass transfer kinetic in osmotic dehydration of kiwifruit |
author |
Jabrayili, Sharokh |
author_facet |
Jabrayili, Sharokh Farzaneh, Vahid Zare, Zahra Bakhshabadi, Hamid Babazadeh, Zahra Mokhtarian, Mohsen Carvalho, Isabel Saraiva de |
author_role |
author |
author2 |
Farzaneh, Vahid Zare, Zahra Bakhshabadi, Hamid Babazadeh, Zahra Mokhtarian, Mohsen Carvalho, Isabel Saraiva de |
author2_role |
author author author author author author |
dc.contributor.none.fl_str_mv |
Sapientia |
dc.contributor.author.fl_str_mv |
Jabrayili, Sharokh Farzaneh, Vahid Zare, Zahra Bakhshabadi, Hamid Babazadeh, Zahra Mokhtarian, Mohsen Carvalho, Isabel Saraiva de |
description |
Osmotic dehydration characteristics of kiwifruit were predicted by different activation functions of an artificial neural network. Osmotic solution concentration (y(1)), osmotic solution temperature (y(2)), and immersion time (y(3)) were considered as the input parameters and solid gain value (x(1)) and water loss value (x(2)) were selected as the outlet parameters of the network. The result showed that logarithm sigmoid activation function has greater performance than tangent hyperbolic activation function for the prediction of osmotic dehydration parameters of kiwifruit. The minimum mean relative error for the solid gain and water loss parameters with one hidden layer and 19 nods were 0.00574 and 0.0062% for logarithm sigmoid activation function, respectively, which introduced logarithm sigmoid function as a more appropriate tool in the prediction of the osmotic dehydration of kiwifruit slices. As a result, it is concluded that this network is capable in the prediction of solid gain and water loss parameters (responses) with the correlation coefficient values of 0.986 and 0.989, respectively. |
publishDate |
2016 |
dc.date.none.fl_str_mv |
2016-04 2016-04-01T00:00:00Z 2017-04-07T15:56:48Z |
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/10400.1/9528 |
url |
http://hdl.handle.net/10400.1/9528 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
0236-8722 AUT: ICA01121; 10.1515/intag-2015-0091 |
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 |
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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1799133243921924096 |