Radial Basis (Fewer Neurons) and Statistical Multiple Linear Regression Models for Predicting Shelf of Processed Cheese
Published: 2013
Author(s) Name: Sumit Goyal, Gyanendra Kumar Goyal |
Author(s) Affiliation: National Dairy Research Institute, Karnal, Haryana, India
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Abstract
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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