Please use this identifier to cite or link to this item: http://hdl.handle.net/1822/4289

TitleLamb meat tenderness prediction using neural networks and sensitivity analysis
Author(s)Cortez, Paulo
Portelinha, Manuel
Rodrigues, Sandra
Cadavez, Vasco
Teixeira, Alfredo
KeywordsRegression
Multilayer perceptrons
Multiple regression
Meat quality
Ensembles
Data mining
Issue dateOct-2005
PublisherEUROSIS-ETI
CitationTEIXEIRA. J. Feliz; BRITO, A., ed. lit. – “ESM’2005 : proceedings of the European Simulation Multiconference, Oporto, Portugal, 2005. [S.l.]: Eurosis, [2005]. ISBN 90-77381-22-8. p. 177-181.
Abstract(s)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.
TypeConference paper
URIhttp://hdl.handle.net/1822/4289
ISBN90-77381-22-8
Peer-Reviewedyes
AccessOpen access
Appears in Collections:CAlg - Artigos em livros de atas/Papers in proceedings
DSI - Engenharia da Programação e dos Sistemas Informáticos

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