Lamb Meat Quality Assessment by Support Vector Machines

Authors:
Paulo Cortez;Manuel Portelinha;Sandra Rodrigues;Vasco Cadavez;Alfredo Teixeira
Affiliations:
Department of Information Systems, University of Minho, Guimarães, Portugal 4800-058;Bragança Polytechnic Institute, Bragança, Portugal 5301-855;Bragança Polytechnic Institute, Bragança, Portugal 5301-855;Bragança Polytechnic Institute, Bragança, Portugal 5301-855;Bragança Polytechnic Institute, Bragança, Portugal 5301-855
Venue:
Neural Processing Letters
Year:
2006

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Abstract

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.