Please use this identifier to cite or link to this item: http://hdl.handle.net/1822/5923

TitleLamb meat quality assessment by support vector machines
Author(s)Cortez, Paulo
Portelinha, Manuel
Rodrigues, Sandra
Cadavez, Vasco
Teixeira, Alfredo
KeywordsRegression
Multilayer perceptrons
Support vector machines
Meat quality
Data mining
Feature selection
Issue date2006
PublisherSpringer
JournalNeural Processing Letters
CitationP. 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.
Abstract(s)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.
TypeArticle
URIhttp://hdl.handle.net/1822/5923
DOI10.1007/s11063-006-9009-6
ISSN1370-4621
Publisher versionhttp://dx.doi.org/10.1007/s11063-006-9009-6
Peer-Reviewedyes
AccessOpen access
Appears in Collections:CAlg - Artigos em revistas internacionais/Papers in international journals
DSI - Engenharia da Programação e dos Sistemas Informáticos

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