Evaluation of pork color by using computer vision

The objective of this study was to determine the potential of computer vision technology for evaluating fresh pork loin color. Software was developed to segment pork loin images into background, muscle and fat. Color image features were then extracted from segmented images. Features used in this study included mean and standard deviation of red, green, and blue bands of the segmented muscle area. Sensory scores were obtained for the color characteristics of the lean meat from a trained panel using a 5-point color scale. The scores were based on visual perception and ranged from 1 to 5. Both statistical and neural network models were employed to predict the color scores by using the image features as inputs. The statistical model used partial least squares technique to derive latent variables. The latent variables were subsequently used in a multiple linear regression. The neural network used a back-propagation learning algorithm. Correlation coefficients between predicted and original sensory scores were 0.75 and 0.52 for neural network and statistical models, respectively. Prediction error was the difference between average sensory score and the predicted color score. An error of 0.6 or lower was considered negligible from a practical viewpoint. For 93.2% of the 44 pork loin samples, prediction error was lower than 0.6 in neural network modeling. In addition, 84.1% of the samples gave an error lower than 0.6 in the statistical predictions. Results of this study showed that an image processing system in conjunction with a neural network is an effective tool for evaluating fresh pork color.