01355nas a2200229   4500000000100000000000100001008004100002260001200043653002800055653003100083653001000114653002100124653001500145100001600160700002600176245008900202856007400291300001000365490000600375520073000381022001401111       2012                            d  c06/201210aArtificial Intelligence10aArtificial Neural Networks10aElman10aProcessed Cheese10aShelf Life1 aSumit Goyal1 aGyanendra Kumar Goyal00aEvaluation of Shelf Life of Processed Cheese by Implementing Neural Computing Models  uhttp://www.ijimai.org/journal/sites/default/files/IJIMAI20121_5_8.pdf  a61-640 v13 aFor predicting the shelf life of processed cheese stored at 7-8 C, Elman single and multilayer models were developed and compared. The input variables used for developing the models were soluble nitrogen, pH; standard plate count, Yeast & mould count, and spore count, while output variable was sensory score. Mean Square Error, Root Mean Square Error, Coefficient of Determination and Nash - Sutcliffo Coefficient were applied in order to compare the prediction ability of the developed models. The Elman models got simulated very well and showed excellent agreement between the experimental data and the predicted values, suggesting that the Elman models can be used for predicting the shelf life of processed cheese.
  a1989-1660