Predicting Shelf Life of Burfi through Soft Computing

Full Text (PDF, 333KB), PP.26-33

Views: 0 Downloads: 0

Author(s)

Sumit Goyal 1,* Gyanendra Kumar Goyal 1

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

* Corresponding author.

DOI: https://doi.org/10.5815/ijieeb.2012.03.04

Received: 3 Apr. 2012 / Revised: 13 May 2012 / Accepted: 2 Jun. 2012 / Published: 8 Jul. 2012

Index Terms

Soft computing, artificial neural networks, artificial intelligence, burfi, shelf life prediction, cascade

Abstract

Soft computing cascade multilayer 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 input variables, and the overall acceptability score was the output variable. The modelling results showed excellent agreement between the experimental data and predicted values, with a high determination coefficient (R2 = 0.993499439) and low RMSE (0.006500561), indicating that the developed model was able to analyze nonlinear multivariate data with very good performance, and can be used for predicting the shelf life of burfi.

Cite This Paper

Sumit Goyal, Gyanendra Kumar Goyal, "Predicting Shelf Life of Burfi through Soft Computing", International Journal of Information Engineering and Electronic Business(IJIEEB), vol.4, no.3, pp.26-33, 2012. DOI:10.5815/ijieeb.2012.03.04

Reference

[1]http://www.learnartificialneuralnetworks.com/ (accessed on 7.1.2011).

[2]http://en.wikipedia.org/wiki/Feedforward_neural_network (accessed on 30.1.2011).

[3]http://www.techbriefs.com/component/content/649?task=view (accessed on 7.1.2011).

[4]www.medlabs.com/Downloads/food_product_shelf_life_web.pdf (accessed on 1.1.2011).

[5]Sofu, A. and Ekinci, F.Y. (2007). Estimation of storage time of yogurt with artificial neural network modeling. Journal of Dairy Science, 90(7), 3118–3125.

[6]Cruz, A.G., Walter, E.H.M., Cadena, R.S., Faria, J.A.F., Bolini, H.M.A. and Fileti, A.M.F. (2009). Monitoring the authenticity of low-fat yogurts by an artificial neural network. Journal of Dairy Science, 92(10), 4797–4804.

[7]Sharifi, M., Rafiee, S., Ahmadi, H. and Rezaee, M. (2011). Prediction of moisture content of bergamot fruit during thin-layer drying using artificial neural networks. Innovative Computing Technology Communications in Computer and Information Science, 241(3), 71-80.

[8]Fathi, M., Mohebbi, M. and Razavi, S.M.A. (2009). Application of image analysis and artificial neural network to predict mass transfer kinetics and color changes of osmotically dehydrated kiwifruit. Food Bioprocess Technology, 4(8), 1357-1366.

[9]Movagharnejad, K. and Nikzad, M. (2007). Modeling of tomato drying using artificial neural network. Computers and Electronics in Agriculture, 59, 78–85.

[10]Torrecilla, J.S., Otero, L. and Sanz, P.D. (2004). A neural network approach for thermal/pressure food processing. Journal of Food Engineering, 62, 89–95.

[11]Goyal, Sumit and Goyal, G.K. (2012). Radial basis (exact fit) and linear layer (design) computerized ANN models for predicting shelf life of processed cheese. Computer Science Journal, 2(1), 11-18. 

[12]Goyal, Sumit and Goyal, G.K. (2012). Artificial neural expert computing models for determining shelf life of processed cheese. International Journal of Electrical and Computer Engineering, 2(3), 31-36. 

[13]Goyal, Sumit and Goyal, G.K. (2012). Shelf life estimation of processed cheese by artificial neural network expert systems. Journal of Advanced Computer Science & Technology, 1(1), 32-41

[14]Goyal, Sumit and Goyal, G.K. (2012). Estimating processed cheese shelf life with artificial neural networks. International Journal of Artificial Intelligence, 1(1), 19-24. 

[15]Goyal, Sumit and Goyal, G.K. (2012). Time-delay artificial neural network computing models for predicting shelf life of processed cheese. BRAIN. Broad Research in Artificial Intelligence and Neuroscience, 3(1), 63-70. 

[16]Goyal, Sumit and Goyal, G.K. (2012). Linear layer and generalized regression computational intelligence models for predicting shelf life of processed cheese. Russian Journal of Agricultural and Socio-Economic Sciences, 3(3), 28-32. 

[17]Goyal, Sumit and Goyal, G.K. (2012). Soft computing single hidden layer models for shelf life prediction of burfi. Russian Journal of Agricultural and Socio-Economic Sciences, 5(5), 28-32. 

[18]Goyal, Sumit and Goyal, G.K. (2012). Time – delay single layer artificial neural network models for estimating shelf life of burfi. International Journal of Research Studies in Computing, 1(2), 11-18. 

[19]Goyal, Sumit and 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.

[20]Goyal, Sumit and Goyal, G.K. (2012). Elman backpropagation single hidden layer models for estimating shelf life of kalakand, Advances in Information Technology and Management, 1(3), pp.127-131. 

[21]Goyal, Sumit and Goyal, G.K. (2012). Shelf life determination of kalakand using soft computing technique. Advances in Computational Mathematics and its Applications, 1(3), 131-135.

[22]Goyal, Sumit and Goyal, G.K. (2012). A novel method for shelf life detection of processed cheese using cascade single and multi layer artificial neural network computing models. ARPN Journal of Systems and Software, 2(2), 79-83.

[23]Goyal, Sumit and 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.