Artificial Neural Network Simulated Elman Models for Predicting Shelf Life of Processed Cheese

Elman artificial neural network models with single and multilayer for predicting shelf life of processed cheese stored at 7-8oC were developed. Input parameters were: Body & texture, aroma & flavour, moisture, and free fatty acid, while sensory score was output parameter. Bayesian regularization was training algorithm for the models. The network was trained up to 100 epochs, and neurons in each hidden layers varied from 1 to 20. Transfer function for hidden layer was tangent sigmoid, while for the output layer it was pure linear function. Mean Square Error, Root Mean Square Error, Coefficient of Determination and Nash Sutcliffo Coefficient were used for comparing the prediction ability of the developed models. Elman model with combination of 4-17-17-1 performed significantly well for predicting the shelf life of processed cheese stored at 7-8o C. DOI: 10.4018/jamc.2012070102 International Journal of Applied Metaheuristic Computing, 3(3), 20-32, July-September 2012 21 Copyright © 2012, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited. the first layer has a recurrent connection. The delay in this connection stores values from the previous time step, which can be used in the current time step. Therefore, even if two Elman models, with the same weights and biases, are given identical inputs at a given time step, their outputs can be different because of different feedback states. Because the network can store information for future reference, it is able to learn temporal patterns as well as spatial patterns. The Elman models can be trained to respond to, and to generate, both kinds of patterns (Demuth et al., 2009). Processed cheese is a dairy product made from medium ripened (up to six months old) Cheddar cheese, and sometimes a part of ripened cheese is replaced by fresh cheese. During its manufacture emulsifiers, extra salt, preservatives, food colorings and spices (if desired) are added, and the mixture is heated to 70o C for 10-15 minutes with steam in a cleaned double jacketed stainless steel kettle, which is open, shallow and round-bottomed, with continuous gentle stirring (about 50-60 circular motions per minute) with a flattened ladle in order to get unique body & texture in the product. Different varieties of processed cheese with many flavours, colors, and textures are available in the world market. Processed cheese has advantages over unprocessed cheese, viz., pleasing taste, extended shelf life, and the use of emulsifiers in processed cheese preparation results in cheese that melts smoothly when heated. Processed cheeses are normally very smooth, medium-firm, velvety, and highly regarded by the cheese lovers. The determination of shelf life of processed cheese in the laboratory is very costly affair and takes a very long time to give results. It is alarming need of the day that artificial neural network technique, which is fully equipped to predict the shelf life of food products, should be employed for processed cheese as well. Hence, a study was planned for developing Elman artificial neural network models with single and multilayer for predicting shelf life of processed cheese stored at 7-8oC. The results of this investigation would be very useful for consumers, dairy factories manufacturing processed cheese, wholesalers, retailers, regulatory authorities, food researchers and academicians.