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Radial Basis (Fewer Neurons) and Statistical Multiple Linear Regression Models for Predicting Shelf of Processed Cheese

International Journal of Knowledge Based Computer Systems

Volume 1 Issue 1

Published: 2013
Author(s) Name: Sumit Goyal, Gyanendra Kumar Goyal | Author(s) Affiliation: National Dairy Research Institute, Karnal, Haryana, India
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Processed Cheese is one of the most popular varieties among the types of cheeses. Radial Basis (Fewer Neurons) and Multiple Linear Regression models were developed for predicting the shelf life of processed cheese stored at 7-8º C by taking body & texture, aroma & flavour, moisture, free fatty acids as input parameters, and sensory score as output parameter. Mean square error, root mean square error, coefficient of determination and Nash–Sutcliffe coefficient were used for calculating the prediction capability of the developed models. The comparison of the two developed models revealed that the performance of radial basis (fewer neurons) artificial neural network model is better than that of statistical multiple linear regression model for predicting the shelf life of processed cheese.

Keywords: Radial Basis (Fewer Neurons), Multiple Linear Regression, Artificial Intelligence, Artificial Neural Network (ANN), Soft Computing

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