LINEAR LAYER AND GENERALIZED REGRESSION COMPUTATIONAL INTELLIGENCE MODELS FOR PREDICTING SHELF LIFE OF PROCESSED CHEESE

S. Goyal, G.K. Goyal
2012 Russian Journal of Agricultural and Socio-Economic Sciences  
This paper highlights the significance of computational intelligence models for predicting shelf life of processed cheese stored at 7-8 o C. Linear Layer and Generalized Regression models were developed with input parameters: Soluble nitrogen, pH, Standard plate count, Yeast & mould count, Spores, and sensory score as output parameter. Mean Square Error, Root Mean Square Error, Coefficient of Determination and Nash -Sutcliffo Coefficient were used in order to compare the prediction ability of
more » ... iction ability of the models. The study revealed that Generalized Regression computational intelligence models are quite effective in predicting the shelf life of processed cheese stored at 7-8 o C.
doi:10.18551/rjoas.2012-03.05 fatcat:x7fjddatfvg43a6rjlsej3hafa