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- Lamb meat tenderness prediction using neural networks and sensitivity analysisPublication . Cortez, Paulo; Portelinha, Manuel; Rodrigues, Sandra; Cadavez, Vasco; Teixeira, AlfredoThe 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.
- Lamb meat quality assessment by support vector machinesPublication . Cortez, Paulo; Portelinha, Manuel; Rodrigues, Sandra; Cadavez, Vasco; Teixeira, AlfredoThe 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.