Modeling sterilization process of canned foods using artificial neural networks
Autor(a) principal: | |
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Data de Publicação: | 2005 |
Outros Autores: | , , |
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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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 |
format |
article |
status_str |
publishedVersion |
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 |
rights_invalid_str_mv |
Elsevier B.V. |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Chemical Engineering and Processing: Process Intensification |
publisher.none.fl_str_mv |
Chemical Engineering and Processing: Process Intensification |
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reponame:LOCUS Repositório Institucional da UFV instname:Universidade Federal de Viçosa (UFV) instacron:UFV |
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Universidade Federal de Viçosa (UFV) |
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LOCUS Repositório Institucional da UFV |
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LOCUS Repositório Institucional da UFV |
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