A Novel Method for Shelf Life Detection of Processed Cheese Using Cascade Single and Multi Layer Artificial Neural Network Computing Models

This paper presents the potential of Cascade Backpropagation algorithm based ANN models in detecting the shelf life of processed cheese stored at 30o C. Processed cheese is a dairy product made from ripened Cheddar cheese and sometimes a part of ripened cheese is replaced by fresh cheese; plus emulsifiers, extra salt, spices and food colorings. The cascade backpropagation algorithm (CBA) is the basis of a conceptual design for accelerating learning in ANNs. In this research input parameters were texture, aroma and flavour, moisture, free fatty acids.Sensory score was taken as output parameter. Bayesian regularization algorithm was used for training the network. Neurons in each hidden layers varied from 1 to 50. The network was trained with 200 epochs with single and multiple hidden layers. Transfer function for hidden layers was tangent sigmoid and pure linear was output function. Mean Square Error, Root Mean Square Error, Coefficient of Determination and Nash - Sutcliffo Coefficient performance measures were used to test the prediction potential of the developed CBA model. CBA model detected 29.13 daysshelf life which is quite close to experimentally obtained shelf life of 30 days suggesting that the product is acceptable.