Machine Learning Elman Technique for Predicting Shelf Life of Burfi

Abstract

Elman artificial neural network single and multilayer computerized models were developed for predicting the shelf life of burfi stored at 30o C. The experimental data of the product relating to moisture, titratable acidity, free fatty acids, tyrosine, and peroxide value were taken as input variables, and overall acceptability score as output variable for developing the models. Bayesian regularization algorithm was applied as training algorithm for neural network. Transfer function for hidden layers was tangent sigmoid; while for output layer it was pure linear function. Elman model with a combination of 5101 and 5771 performed exceedingly well for predicting the shelf life of burfi.