Predicción del tiempo de almacenamiento de carne congelada usando modelado de redes neuronales artificiales con valores de color

Abstract

Among the various methods available to determine the storage time of frozen meat, including analyses based on physical and chemical properties, sensory analysis, particularly color changes, is an important aspect of meat acceptability for consumers. In this study, an artificial neural network (ANN) was employed to predict the storage time of the meat based on the CIELAB color space, represented by the Lab* (L*), (a*), and (b*) values measured by a computer vision system at two–month intervals over a period of up to one year. The ANN topology was optimized based on changes in correlation coefficients (R2) and mean square errors (MSE), resulting in a network of 60 neurons in a hidden layer (R2 = 0.9762 and MSE = 0.0047). The ANN model's performance was evaluated using criteria such as mean absolute deviation (MAD), MSE, root mean square error (RMSE), R2, and mean absolute error (MAE), which were found to be 0.0344, 0.0047, 0.0687, 0.9762, and 0.0078, respectively. Overall, these results suggest that using a computer vision–based system combined with artificial intelligence could be a reliable and nondestructive technique for evaluating meat quality throughout its storage time.