Machine Learning Elman Technique for Predicting Shelf Life of Burfi

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Sumit Goyal 1,* Gyanendra Kumar Goyal 1

1. National Dairy Research Institute, Karnal -132001, India

* Corresponding author.


Received: 17 Mar. 2012 / Revised: 10 Apr. 2012 / Accepted: 23 May 2012 / Published: 8 Jul. 2012

Index Terms

Elman, Artificial Neural Network, Artificial Intelligence, Burfi, Shelf Life Prediction


Elman artificial neural network single and multilayer computerized models were developed for predicting the shelf life of burfi stored at 30ºC. The experimental data of the product relating to moisture, titratable acidity, free fatty acids, tyrosine, and peroxide value were taken as input variables, and overall acceptability score as output variable for developing the models. Bayesian regularization algorithm was applied as training algorithm for neural network. Transfer function for hidden layers was tangent sigmoid; while for output layer it was pure linear function. Elman model with a combination of 5→10→1 and 5→7→7→1 performed exceedingly well for predicting the shelf life of burfi.

Cite This Paper

Sumit Goyal, Gyanendra Kumar Goyal, "Machine Learning Elman Technique for Predicting Shelf Life of Burfi", International Journal of Modern Education and Computer Science (IJMECS), vol.4, no.7, pp.17-23, 2012. DOI:10.5815/ijmecs.2012.07.03


[1]Computerworld Website, 2011: (accessed on 3.1.2011).
[2]H.Demuth, M.Beale, M. Hagan - Neural network toolbox user’s guide,The MathWorks Inc., Natrick, USA, 2009.
[3]R.C.Martins, V.V. Lopes, A.A. Vicente, and J.A. Teixeira,” Computational shelf-life dating: complex systems approaches to food quality and safety,” Food and Bioprocess Technology, vol.1, no.3, pp. 207-222, 2008.
[4]Sumit Goyal and G.K. Goyal,”Brain based artificial neural network scientific computing models for shelf life prediction of cakes,” Canadian Journal on Artificial Intelligence, Machine Learning and Pattern Recognition, vol. 2, no.6, pp.73-77, 2011.
[5]Sumit Goyal and G.K. Goyal,”Simulated neural network intelligent computing models for predicting shelf life of soft cakes,” Global Journal of Computer Science and Technology, vol.11, no.14,version 1.0, pp. 29-33, 2011.
[6]Sumit Goyal and G.K. Goyal,”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, vol.2, no.5, pp.292-295, 2011.
[7]Sumit Goyal and G.K. Goyal,”Application of artificial neural engineering and regression models for forecasting shelf life of instant coffee drink,” International Journal of Computer Science Issues, vol.8(4), no.1, pp. 320-324, 2011.
[8]Sumit Goyal and G.K. Goyal,”Cascade and feedforward backpropagation artificial neural networks models for prediction of sensory quality of instant coffee flavoured sterilized drink,” Canadian Journal on Artificial Intelligence, Machine Learning and Pattern Recognition, vol.2, no.6, pp. 78-82, 2011.
[9]Sumit Goyal and G.K. Goyal,”Development of neuron based artificial intelligent scientific computer engineering models for estimating shelf life of instant coffee sterilized drink,” International Journal of Computational Intelligence and Information Security, vol.2, no.7, pp.4-12,2011.
[10]Sumit Goyal and G.K. Goyal,”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, vol. 2, no.9, pp. 1-4, 2011.
[11]Sumit Goyal and G.K. Goyal,”Development of intelligent computing expert system models for shelf life prediction of soft mouth melting milk cakes,” International Journal of Computer Applications, vol.25, no.9, pp.41-44,2011.
[12]Sumit Goyal and G.K. Goyal,”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, vol. 2, no.3, pp.434-438, 2011.
[13]Z.P. Efstathios, R.M. Fady, A.A. Argyria, M.B. Conrad and E. N. George-John,”A comparison of artificial neural networks and partial least squares modelling for the rapid detection of the microbial spoilage of beef fillets based on Fourier transform infrared spectral fingerprints,” Food Microbiology, vol.28, no.4, pp.782–790, 2011.
[14]D. Colorado, M.E. Ali, O. García-Valladares, O. and J.A. Hernández,”Heat transfer using a correlation by neural network for natural convection from vertical helical coil in oil and glycerol/water solution,” Energy, vol.36, pp. 854-863, 2011.
[15]P. Rai, G.C. Majumdar, S. DasGupta and S. De,” Prediction of the viscosity of clarified fruit juice using artificial neural network: a combined effect of concentration and temperature,” Journal of Food Engineering, vol. 68, pp. 527–533, 2005.
[16]A. Khoshhal, A.A. Dakhel, A. Etemadi and S. Zereshki,” Artificial neural network modeling of apple drying process,” Journal of Food Process Engineering, vol.33, pp. 298–313, 2010.
[17]M. Fathi, M. Mohebbi and S.M.A. Razavi,” Application of Image Analysis and Artificial Neural Network to Predict Mass Transfer Kinetics and Color Changes of Osmotically Dehydrated Kiwifruit,” Food Bioprocess Technology, vol. 4, no.8, pp.1357-1366, 2009.
[18]N. Raharitsifa and C. Ratti,” Foam-mat freeze-drying of apple juice part 1: Experimental data and ANN simulations,” Journal of Food Process Engineering, vol. 33, pp. 268–283, 2010.