Figure 3. Comparison of ASS and PSS single layer model-Time-Delay Artificial Neural Network Computing Models for Predicting Shelf Life of Processed Cheese

<p>TDNN models with single and multi layers were developed taking soluble nitrogen, pH,<br> standard plate count, yeast & mould count, spore count as input parameters, and sensory score as<br> output parameter for predicting the shelf life of processed cheese stored at 30o C. Mean Square<br> Error, Root Mean Square Error, Coefficient of Determination and Nash - Sutcliffo Coefficient were<br> used in order to compare the prediction ability of the developed TDNN models. Regression<br> equations were developed for predicting the shelf life of processed cheese, which came out as 28.25<br> days. Since, predicted value is close to the experimentally determined shelf life of 30 days, hence<br> from the study it can be concluded that TDNN artificial neural network models are quite efficient in<br> predicting shelf life of processed cheese.