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Linear layer and Generalized Regression computational intelligence models for predicting shelf life of processed cheese

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  • Goyal Sumit

    (National Dairy Research Institute)

  • Goyal Gyanendra Kumar

    (National Dairy Research Institute)

Abstract

This paper highlights the significance of computational intelligence models for predicting shelf life of processed cheese stored at 7-8oC. 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 the models. The study revealed that Generalized Regression computational intelligence models are quite effective in predicting the shelf life of processed cheese stored at 7-8oC.

Suggested Citation

  • Goyal Sumit & Goyal Gyanendra Kumar, 2012. "Linear layer and Generalized Regression computational intelligence models for predicting shelf life of processed cheese," Russian Journal of Agricultural and Socio-Economic Sciences, CyberLeninka;Редакция журнала Russian Journal of Agricultural and Socio-Economic Sciences, vol. 3(3), pages 28-32.
    • Handle: RePEc:scn:031261:14030135
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