Lamb meat tenderness prediction using neural networks and sensitivity analysis

• Author(s): Cortez, Paulo, 1971- ; Portelinha, Manuel ; Rodrigues, Sandra ; Cadavez, Vasco ; Teixeira, Alfredo
• Date: 2005
• Persistent ID: http://hdl.handle.net/1822/4289
• Origin: RepositóriUM - Universidade do Minho
• Subject(s): Regression; Multilayer perceptrons; Multiple regression; Meat quality; Ensembles; Data mining
• Document Type: Conference Object
• Language: English

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.