Abstract—The purpose of this study is to develop artificial neural network (ANN) models for predicting shelf life of processed cheese stored at 7-8ºC. Body & texture, aroma & flavour, moisture and free fatty acids were taken as input parameters, and sensory score as output parameter for developing the models. The developed Cascade single layer ANN models were compared with each other. Bayesian regularization was used for training ANN models. Network was trained with 100 epochs, and neurons in each hidden layer(s) varied from 3 to 20. Cascade ANN models very well predicted the shelf life of processed cheese.
Index Terms—Artificial Intelligence, Cascade, Artificial neural networks (ANN), Processed Cheese, Shelf Life, Soft Computing
Cite: Gyanendra Kumar Goyal and Sumit Goyal, "Cascade Artificial Neural Network Models for Predicting Shelf Life of Processed Cheese," Journal of Advances in Information Technology, Vol. 4, No. 2, pp. 80-83, May, 2013.doi:10.4304/jait.4.2.80-83
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