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Please use this identifier to cite or link to this item: http://hdl.handle.net/10198/865

Título: Lamb meat tenderness prediction using neural networks and sensitivity analysis
Autor: Cortez, Paulo
Portelinha, Manuel
Rodrigues, Sandra
Cadavez, Vasco
Teixeira, A.
Palavras-chave: Regression
Multilayer perceptrons
Multiple regression
Meat quality
Ensembles
Data mining
Issue Date: 2005
Editora: Eurosis
Citação: 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
Resumo: 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.
URI: http://hdl.handle.net/10198/865
Appears in Collections:CA - Publicações em Proceedings Indexadas ao ISI

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