Lamb meat quality assessment by support vector machines
Autor(a) principal: | |
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Data de Publicação: | 2006 |
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/1822/5923 |
Resumo: | The correct assessment of meat quality (i.e., to fulfill the consumer's needs) is crucial element within the meat industry. Although there are several factors that affect the perception of taste, tenderness is considered the most important characteristic. In this paper, a Feature Selection procedure, based on a Sensitivity Analysis, is combined with a Support Vector Machine, in order to predict lamb meat tenderness. This real-world problem is defined in terms of two difficult regression tasks, by modeling objective (e.g. Warner-Bratzler Shear force) and subjective (e.g. human taste panel) measurements. In both cases, the proposed solution is competitive when compared with other neural (e.g. Multilayer Perceptron) and Multiple Regression approaches. |
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Lamb meat quality assessment by support vector machinesRegressionMultilayer perceptronsSupport vector machinesMeat qualityData miningFeature selectionScience & TechnologyThe correct assessment of meat quality (i.e., to fulfill the consumer's needs) is crucial element within the meat industry. Although there are several factors that affect the perception of taste, tenderness is considered the most important characteristic. In this paper, a Feature Selection procedure, based on a Sensitivity Analysis, is combined with a Support Vector Machine, in order to predict lamb meat tenderness. This real-world problem is defined in terms of two difficult regression tasks, by modeling objective (e.g. Warner-Bratzler Shear force) and subjective (e.g. human taste panel) measurements. In both cases, the proposed solution is competitive when compared with other neural (e.g. Multilayer Perceptron) and Multiple Regression approaches.SpringerUniversidade do MinhoCortez, PauloPortelinha, ManuelRodrigues, SandraCadavez, VascoTeixeira, Alfredo20062006-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/1822/5923engP. Cortez, M. Portelinha, S. Rodrigues, V. Cadavez and A. Teixeira. Lamb Meat Quality Assessment by Support Vector Machines. In Neural Processing Letters, Springer, 24 (1): 41-51, 2006. ISSN:1370-4621.1370-462110.1007/s11063-006-9009-6http://dx.doi.org/10.1007/s11063-006-9009-6info: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:RCAAP2023-07-21T12:31:43Zoai:repositorium.sdum.uminho.pt:1822/5923Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T19:26:59.930148Repositório 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 |
Lamb meat quality assessment by support vector machines |
title |
Lamb meat quality assessment by support vector machines |
spellingShingle |
Lamb meat quality assessment by support vector machines Cortez, Paulo Regression Multilayer perceptrons Support vector machines Meat quality Data mining Feature selection Science & Technology |
title_short |
Lamb meat quality assessment by support vector machines |
title_full |
Lamb meat quality assessment by support vector machines |
title_fullStr |
Lamb meat quality assessment by support vector machines |
title_full_unstemmed |
Lamb meat quality assessment by support vector machines |
title_sort |
Lamb meat quality assessment by support vector machines |
author |
Cortez, Paulo |
author_facet |
Cortez, Paulo Portelinha, Manuel Rodrigues, Sandra Cadavez, Vasco Teixeira, Alfredo |
author_role |
author |
author2 |
Portelinha, Manuel Rodrigues, Sandra Cadavez, Vasco Teixeira, Alfredo |
author2_role |
author author author author |
dc.contributor.none.fl_str_mv |
Universidade do Minho |
dc.contributor.author.fl_str_mv |
Cortez, Paulo Portelinha, Manuel Rodrigues, Sandra Cadavez, Vasco Teixeira, Alfredo |
dc.subject.por.fl_str_mv |
Regression Multilayer perceptrons Support vector machines Meat quality Data mining Feature selection Science & Technology |
topic |
Regression Multilayer perceptrons Support vector machines Meat quality Data mining Feature selection Science & Technology |
description |
The correct assessment of meat quality (i.e., to fulfill the consumer's needs) is crucial element within the meat industry. Although there are several factors that affect the perception of taste, tenderness is considered the most important characteristic. In this paper, a Feature Selection procedure, based on a Sensitivity Analysis, is combined with a Support Vector Machine, in order to predict lamb meat tenderness. This real-world problem is defined in terms of two difficult regression tasks, by modeling objective (e.g. Warner-Bratzler Shear force) and subjective (e.g. human taste panel) measurements. In both cases, the proposed solution is competitive when compared with other neural (e.g. Multilayer Perceptron) and Multiple Regression approaches. |
publishDate |
2006 |
dc.date.none.fl_str_mv |
2006 2006-01-01T00:00:00Z |
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/1822/5923 |
url |
http://hdl.handle.net/1822/5923 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
P. Cortez, M. Portelinha, S. Rodrigues, V. Cadavez and A. Teixeira. Lamb Meat Quality Assessment by Support Vector Machines. In Neural Processing Letters, Springer, 24 (1): 41-51, 2006. ISSN:1370-4621. 1370-4621 10.1007/s11063-006-9009-6 http://dx.doi.org/10.1007/s11063-006-9009-6 |
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.publisher.none.fl_str_mv |
Springer |
publisher.none.fl_str_mv |
Springer |
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 |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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RCAAP |
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RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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