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