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For predicting the shelf life of processed cheesestored at 7-8o C, Elman single and multilayer models weredeveloped and compared. The input variables used for developingthe models were soluble nitrogen, pH; standard plate count, Yeast& mould count, and spore count, while output variable wassensory score. Mean Square Error, Root Mean Square Error,Coefficient of Determination and Nash - Sutcliffo Coefficientwere applied in order to compare the prediction ability of thedeveloped models. The Elman models got simulated very well andshowed excellent agreement between the experimental data andthe predicted values, suggesting that the Elman models can beused for predicting the shelf life of processed cheese.

Evaluation of Shelf Life of Processed Cheese by Implementing Neural Computing Models