Percorrer por autor "Cortez, Paulo"
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- 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.
- 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.
- Previsão da tenrura da carne de cordeiro via redes neuronais artificiais e análise de sensibilidadePublication . Portelinha, Manuel; Cortez, Paulo; Rodrigues, Sandra; Cadavez, Vasco; Teixeira, AlfredoA avaliação da qualidade é um factor chave para a indústria da carne, onde o objectivo primordial reside na satisfação das necessidades dos consumidores. Em particular, a tenrura é considerada a mais importante característica que afecta o paladar da carne. Neste trabalho, é proposto um Conjunto de Redes Neuronais1, baseado na selecção de atributos via um procedimento de Análise de Sensibilidade, para a predição da tenrura da carne de cordeiros. Este problema foi modelado através de duas tarefas diferentes de regressão, usando medições instrumentais e um painel sensorial. Em ambos os casos, as soluções propostas apresentaram melhores resultados do que o método tradicional da Regressão Múltipla.
- Using multiple regression, neural networks and supprot vector machines to predict lamb carcasses compositionPublication . Silva, Filipe; Cortez, Paulo; Cadavez, VascoThe objective of this work was to use a Data Mining (DM) approach to predict, using as predictors the carcass measurements taken at slaughter line, the composition of lamb carcasses. One hundred and twenty ve lambs of Churra Galega Bragan cana breed were slaughtered. During carcasses quartering, a caliper was used to perform subcutaneous fat measurements, over the maximum depth of longissimus muscle (LM), between the 12th and 13th ribs (C12), and between the 1st and 2nd lumbar vertebrae (C1). The Muscle (MP), Bone (BP), Subcutaneous Fat (SFP), Inter-Muscular Fat (IFP), and Kidney Knob and Channel Fat (KKCF) proportions of lamb carcasses were computed. We used the rminer R library and compared three regression techniques: Multiple Regression (MR), Neural Networks (NN) and Support Vector Machines (SVM). The SVM model provided the lowest relative absolute error for the prediction of BP, SFP and KKCF, while MR presented the best predictions for MP and IFP. Also, a sensitivity analysis procedure revealed the C12 measurement as the most relevant predictor for all ve carcass tissues.