Intelligent Artificial Neural Network computing models for predicting shelf life of processed cheese

Abstract

This paper presents the capability and potential of artificial neural network models for determining the shelf life of processed cheese stored at 30°C. Archaeologists have discovered that as far back as 6000 BC raw cheese had been made from cow's and goat's milk. Since past many decades ripened cheese prepared from cow's milk is converted into processed cheese as a value added product, which has great demand due to its unique body and texture, aroma and flavour, and sensory attributes. Radial basis and multiple linear regression models were developed and compared with each other. Based on the results further regression equations were developed and solved, Mean Square Error, Root Mean Square Error, Coefficient of Determination and Nash-Sutcliffo Coefficient performance measures were used for testing prediction potential of the models. MLR model was observed to be superior to radial basis model for predicting the shelf life of processed cheese.