Lamb meat tenderness prediction using neural networks and sensitivity analysis
Main Author: | |
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Publication Date: | 2005 |
Other Authors: | , , , |
Language: | eng |
Source: | Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) |
Download full: | http://hdl.handle.net/10198/865 |
Summary: | The assessment of quality is a key factor for the meat industry, where the aim is to fulfill the consumer’s needs. In particular, tenderness is considered the most important characteristic affecting consumer perception of taste. In this paper, a Neural Network Ensemble, with feature selection based on a Sensitivity Analysis procedure, is proposed to predict lamb meat tenderness. This difficult real-world problem is defined in terms of two regression tasks, by using instrumental measurements and a sensory panel. In both cases, the proposed solution outperformed other neural approaches and the Multiple Regression method. |
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Lamb meat tenderness prediction using neural networks and sensitivity analysisRegressionMultilayer perceptronsMultiple regressionMeat qualityEnsemblesData miningThe assessment of quality is a key factor for the meat industry, where the aim is to fulfill the consumer’s needs. In particular, tenderness is considered the most important characteristic affecting consumer perception of taste. In this paper, a Neural Network Ensemble, with feature selection based on a Sensitivity Analysis procedure, is proposed to predict lamb meat tenderness. This difficult real-world problem is defined in terms of two regression tasks, by using instrumental measurements and a sensory panel. In both cases, the proposed solution outperformed other neural approaches and the Multiple Regression method.EurosisBiblioteca Digital do IPBCortez, PauloPortelinha, ManuelRodrigues, SandraCadavez, VascoTeixeira, Alfredo2008-09-17T08:41:00Z20052005-01-01T00:00:00Zconference objectinfo:eu-repo/semantics/publishedVersionapplication/pdfhttp://hdl.handle.net/10198/865engCortez, P.; Portelinha, M.; Rodrigues, Sandra; Cadavez, Vasco; Teixeira, Alfredo (2005). Lamb meat tenderness prediction using neural networks and sensitivity analysis. In Proceedings of the 19 th European Simulation Multiconference - ESM'2005. Porto. p. 177-181info:eu-repo/semantics/openAccessreponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiainstacron:RCAAP2025-02-25T11:54:25Zoai:bibliotecadigital.ipb.pt:10198/865Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireinfo@rcaap.ptopendoar:https://opendoar.ac.uk/repository/71602025-05-28T11:15:40.624400Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiafalse |
dc.title.none.fl_str_mv |
Lamb meat tenderness prediction using neural networks and sensitivity analysis |
title |
Lamb meat tenderness prediction using neural networks and sensitivity analysis |
spellingShingle |
Lamb meat tenderness prediction using neural networks and sensitivity analysis Cortez, Paulo Regression Multilayer perceptrons Multiple regression Meat quality Ensembles Data mining |
title_short |
Lamb meat tenderness prediction using neural networks and sensitivity analysis |
title_full |
Lamb meat tenderness prediction using neural networks and sensitivity analysis |
title_fullStr |
Lamb meat tenderness prediction using neural networks and sensitivity analysis |
title_full_unstemmed |
Lamb meat tenderness prediction using neural networks and sensitivity analysis |
title_sort |
Lamb meat tenderness prediction using neural networks and sensitivity analysis |
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 |
Biblioteca Digital do IPB |
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 Multiple regression Meat quality Ensembles Data mining |
topic |
Regression Multilayer perceptrons Multiple regression Meat quality Ensembles Data mining |
description |
The assessment of quality is a key factor for the meat industry, where the aim is to fulfill the consumer’s needs. In particular, tenderness is considered the most important characteristic affecting consumer perception of taste. In this paper, a Neural Network Ensemble, with feature selection based on a Sensitivity Analysis procedure, is proposed to predict lamb meat tenderness. This difficult real-world problem is defined in terms of two regression tasks, by using instrumental measurements and a sensory panel. In both cases, the proposed solution outperformed other neural approaches and the Multiple Regression method. |
publishDate |
2005 |
dc.date.none.fl_str_mv |
2005 2005-01-01T00:00:00Z 2008-09-17T08:41:00Z |
dc.type.driver.fl_str_mv |
conference object |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10198/865 |
url |
http://hdl.handle.net/10198/865 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Cortez, P.; Portelinha, M.; Rodrigues, Sandra; Cadavez, Vasco; Teixeira, Alfredo (2005). Lamb meat tenderness prediction using neural networks and sensitivity analysis. In Proceedings of the 19 th European Simulation Multiconference - ESM'2005. Porto. p. 177-181 |
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 |
Eurosis |
publisher.none.fl_str_mv |
Eurosis |
dc.source.none.fl_str_mv |
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RCAAP |
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RCAAP |
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Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) |
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Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) |
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Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia |
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