LAUSR

Beef meat freshness was evaluated using artificial vision technique and pattern recognition algorithms. Color and texture features were extracted from the saturation images. The wavelet transform was used to characterize texture and a range of features was used to better characterize color. Two classes of beef meat samples were obtained from the projection of color, texture, and color associated with texture datasets using Principal Component Analysis (PCA) method. The first class corresponds to fresh beef meat samples that have undergone 6 days of cold storage and the second class presents spoiled meat. Probabilistic Neural Network (PNN) and Linear Discriminant Analysis (LDA) algorithms were used to classify and predict beef meat samples into fresh or spoiled samples. Results show that the classification and identification rates obtained by PNN are superior to LDA algorithm using the datasets of color, texture, and color associated with texture. In addition, results show that texture features associated with color features give the best classification and identification rates. An implementation of all proposed algorithms was carried out on a real time embedded system.