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


Affiliations
1 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 7-8 °C by taking body & texture, aroma & flavour, moisture, free fatty acids as input parameters, and sensory score as output parameter. Mean square error, ischolar_main 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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  • http://www.computerworld.com/s/article/57545/Artificial_Neural_Networks (accessed on January 1, 2011).

  • Mateo, F., Gadea, R., Medina, A., Mateo, R. & Jimenez, M. (2009). Predictive assessment of ochratoxin an accumulation in grape juice based-medium by Aspergillus carbonarius using neural networks. Journal of Applied Microbiology, 107(3), 915-927.

  • http://www.law.uchicago.edu/files/files/20.Sykes. Regression.pdf (accssed on January 1, 2011).

  • Martins, R. C., Lopes, V. V., Vicente, A. A. & Teixeira, J. A. (2008). Computational shelf-life dating: Complex systems approaches to food quality and safety. Food and Bioprocess Technology, 1(3), 207-222.

  • Goyal, S. & Goyal, G. K. (2011). A new scientific approach of intelligent artificial neural network engineering for predicting shelf life of milky white dessert jeweled with pistachio. International Journal of Scientific and Engineering Research, 2(9), 1-4.

  • Goyal, S. & Goyal, G. K. (2011). Advanced computing research on cascade single and double hidden layers for detecting shelf life of Kalakand: An artificial neural network approach. International Journal of Computer Science & Emerging Technologies, 2(5), 292-295.

  • Goyal, S. & Goyal, G. K. (2011). Cascade and feedforward backpropagation artificial neural networksmodels for prediction of sensory quality of instant coffee flavoured sterilized drink. Canadian Journal on Artificial Intelligence, Machine Learning and Pattern Recognition, 2(6), 78-82.

  • Goyal, S. & Goyal, G. K. (2011). Application of artificial neural engineering and regression models for forecasting shelf life of instant coffee drink. International Journal of Computer Science Issues, 8(4), 320-324.

  • Goyal, S. & Goyal, G. K. (2012). Study on single and double hidden layers of cascade artificial neural intelligence neurocomputing models for predicting sensory quality of roasted coffee flavoured sterilized drink. International Journal of Applied Information Systems,1(3), 1-4.

  • Goyal, S. & Goyal, G. K. (2011). Development of intelligent computing expert system models for shelf life prediction of soft mouth melting milk cakes. International Journal of Computer Applications, 25(9), 41-44.

  • Goyal, S. & Goyal, G. K. (2011). Simulated neural network intelligent computing models for predicting shelf life of soft cakes. Global Journal of Computer Science and Technology. 11(14), 29-33.

  • Goyal, S. & Goyal, G. K. (2011). Radial basis artificial neural network computer engineering approach for predicting shelf life of brown milk cakes decorated with almonds. International Journal of Latest Trends in Computing, 2(3), 434-438.

  • Goyal, S. & Goyal, G. K. (2011). Brain based artificial neural network scientific computing models for shelf life prediction of cakes. Canadian Journal on Artificial Intelligence, Machine Learning and Pattern Recognition, 2(6), 73-77.

  • Goyal, S., Sitanshu, K. & Goyal, G. K. (2013). Artificial neural networks for analyzing solubility index of roller dried goat whole milk powder. International Journal of Mechanical Engineering and Computer Applications, 1(1), 1-4.

  • Goyal, S. & Goyal, G. K. (2013). Cascade modeling for predicting solubility index of roller dried goat whole milk powder. Bulletin of Electrical Engineering and Informatics, 22(1), 19-28.

  • Goyal, S. & Goyal, G. K. (2013). Intelligent artificial neural network computing models for predicting shelf life of processed cheese. Intelligent Decision Technologies, 7(2), 107-111.

  • Goyal, G. K. & Goyal, S. (2013). Cascade artificial neural network models for predicting shelf life of processed cheese. Journal of Advances in Information Technology, 4(2), 80-83.

  • Goyal, S. & Goyal, G. K. (2013). Artificial vision for estimating shelf life of burfi. Journal of Nutritional Ecology and Food Research, 1(2), 1-3.

  • Goyal, S. (2013). Artificial neural networks (ANNs) in food science - A review. International Journal of Scientific World, 1(2), 19-28.

  • Goyal, S. (2003). Artificial neural networks in vegetables: A comprehensive review. Scientific Journal of Crop Science, 2(7), 75-94.


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

Abstract Views: 336  |  PDF Views: 4

Authors

Sumit Goyal
National Dairy Research Institute, Karnal, Haryana., India

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 7-8 °C by taking body & texture, aroma & flavour, moisture, free fatty acids as input parameters, and sensory score as output parameter. Mean square error, ischolar_main 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

References