Potential of artificial neural network technology for predicting shelf life of processed cheese

Radial basis (fewer neurons) artificial neural network (ANN) models were developed for predicting the shelf life of processed cheese stored at 7-8o C. Mean square error, root mean square error, coefficient of determination and nash - sutcliffo coefficient were applied in order to compare the prediction ability of the developed models. Soluble nitrogen, pH; standard plate count, yeast & mouldcount, and spore count were the input parameters, while sensory score was output parameter for the developed model. The developed model showed very good correlation between actual data and predicted data with high coefficient of determination and nash - sutcliffo coefficient besides low root mean square error, suggesting that the developed model is quite efficient in predicting the shelf life of processed cheese.