Dynamic Scientific Method for Predicting Shelf Life of Buffalo Milk Dairy Product

Abstract

Feedforward multilayer machine learning models were developed to predict the shelf life of burfi stored at 30^oC. Experimental data of the product relating to moisture, titratable acidity, free fatty acids, tyrosine, and peroxide value were input variables, and the overall acceptability score was the output. Bayesian regularization algorithm was used for training the network. The transfer function for hidden layers was tangent sigmoid, and for the output layer it was purelinear function. The network was trained with 100 epochs, and neurons in each hidden layers varied from 3:3 to 20:20. Excellent agreement was found between the actual and predicted values establishing that feedforward multilayer machine learning models are efficient in predicting the shelf life of burfi.