Modeling sterilization process of canned foods using artificial neural networks

Detalhes bibliográficos
Autor(a) principal: Gonçalves, E. C.
Data de Publicação: 2005
Outros Autores: Minim, L. A., Coimbra, J. S. R., Minim, V. P. R.
Tipo de documento: Artigo
Idioma: eng
Título da fonte: LOCUS Repositório Institucional da UFV
Texto Completo: https://doi.org/10.1016/j.cep.2005.04.001
http://www.locus.ufv.br/handle/123456789/22147
Resumo: In order to model the thermal processing of canned foods, the neural networks technique was applied, whose aim was to determine the cold point temperature based on the initial process conditions and the retort's temperature. The network had the following input variables: the processing time, the retort's and cold point's temperature at the current time ti, and at previous times ti−1 and ti−2. The output variable was the temperature of the cold point at the time ti+1. For training the network, a time/temperature data set was obtained through the product processing in a vertical retort. The back-propagation through time and Jordan networks were trained and its generalization performance were compared. In this work, a better generalization capacity were obtained using the back-propagation through time network, which presented an average relative error of 2.2% between the calculated and predicted F values. The architecture of the selected network was the 5-8-9-1.
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spelling Gonçalves, E. C.Minim, L. A.Coimbra, J. S. R.Minim, V. P. R.2018-10-04T18:08:18Z2018-10-04T18:08:18Z2005-1202552701https://doi.org/10.1016/j.cep.2005.04.001http://www.locus.ufv.br/handle/123456789/22147In order to model the thermal processing of canned foods, the neural networks technique was applied, whose aim was to determine the cold point temperature based on the initial process conditions and the retort's temperature. The network had the following input variables: the processing time, the retort's and cold point's temperature at the current time ti, and at previous times ti−1 and ti−2. The output variable was the temperature of the cold point at the time ti+1. For training the network, a time/temperature data set was obtained through the product processing in a vertical retort. The back-propagation through time and Jordan networks were trained and its generalization performance were compared. In this work, a better generalization capacity were obtained using the back-propagation through time network, which presented an average relative error of 2.2% between the calculated and predicted F values. The architecture of the selected network was the 5-8-9-1.engChemical Engineering and Processing: Process Intensificationv. 44, n. 12, p. 1269- 1276, dez. 2005Elsevier B.V.info:eu-repo/semantics/openAccessSterilizationArtificial neural networksCanned foodModeling sterilization process of canned foods using artificial neural networksinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfreponame:LOCUS Repositório Institucional da UFVinstname:Universidade Federal de Viçosa (UFV)instacron:UFVORIGINALartigo.pdfartigo.pdftexto completoapplication/pdf451473https://locus.ufv.br//bitstream/123456789/22147/1/artigo.pdf4651f2456a969bb4ee408b8a0d7dfb80MD51LICENSElicense.txtlicense.txttext/plain; charset=utf-81748https://locus.ufv.br//bitstream/123456789/22147/2/license.txt8a4605be74aa9ea9d79846c1fba20a33MD52THUMBNAILartigo.pdf.jpgartigo.pdf.jpgIM Thumbnailimage/jpeg3987https://locus.ufv.br//bitstream/123456789/22147/3/artigo.pdf.jpgb62205ab0795c0c89f817cbc25c9720cMD53123456789/221472018-10-04 23:00:43.488oai:locus.ufv.br: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Repositório InstitucionalPUBhttps://www.locus.ufv.br/oai/requestfabiojreis@ufv.bropendoar:21452018-10-05T02:00:43LOCUS Repositório Institucional da UFV - Universidade Federal de Viçosa (UFV)false
dc.title.en.fl_str_mv Modeling sterilization process of canned foods using artificial neural networks
title Modeling sterilization process of canned foods using artificial neural networks
spellingShingle Modeling sterilization process of canned foods using artificial neural networks
Gonçalves, E. C.
Sterilization
Artificial neural networks
Canned food
title_short Modeling sterilization process of canned foods using artificial neural networks
title_full Modeling sterilization process of canned foods using artificial neural networks
title_fullStr Modeling sterilization process of canned foods using artificial neural networks
title_full_unstemmed Modeling sterilization process of canned foods using artificial neural networks
title_sort Modeling sterilization process of canned foods using artificial neural networks
author Gonçalves, E. C.
author_facet Gonçalves, E. C.
Minim, L. A.
Coimbra, J. S. R.
Minim, V. P. R.
author_role author
author2 Minim, L. A.
Coimbra, J. S. R.
Minim, V. P. R.
author2_role author
author
author
dc.contributor.author.fl_str_mv Gonçalves, E. C.
Minim, L. A.
Coimbra, J. S. R.
Minim, V. P. R.
dc.subject.pt-BR.fl_str_mv Sterilization
Artificial neural networks
Canned food
topic Sterilization
Artificial neural networks
Canned food
description In order to model the thermal processing of canned foods, the neural networks technique was applied, whose aim was to determine the cold point temperature based on the initial process conditions and the retort's temperature. The network had the following input variables: the processing time, the retort's and cold point's temperature at the current time ti, and at previous times ti−1 and ti−2. The output variable was the temperature of the cold point at the time ti+1. For training the network, a time/temperature data set was obtained through the product processing in a vertical retort. The back-propagation through time and Jordan networks were trained and its generalization performance were compared. In this work, a better generalization capacity were obtained using the back-propagation through time network, which presented an average relative error of 2.2% between the calculated and predicted F values. The architecture of the selected network was the 5-8-9-1.
publishDate 2005
dc.date.issued.fl_str_mv 2005-12
dc.date.accessioned.fl_str_mv 2018-10-04T18:08:18Z
dc.date.available.fl_str_mv 2018-10-04T18:08:18Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
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dc.identifier.uri.fl_str_mv https://doi.org/10.1016/j.cep.2005.04.001
http://www.locus.ufv.br/handle/123456789/22147
dc.identifier.issn.none.fl_str_mv 02552701
identifier_str_mv 02552701
url https://doi.org/10.1016/j.cep.2005.04.001
http://www.locus.ufv.br/handle/123456789/22147
dc.language.iso.fl_str_mv eng
language eng
dc.relation.ispartofseries.pt-BR.fl_str_mv v. 44, n. 12, p. 1269- 1276, dez. 2005
dc.rights.driver.fl_str_mv Elsevier B.V.
info:eu-repo/semantics/openAccess
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publisher.none.fl_str_mv Chemical Engineering and Processing: Process Intensification
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