Thermal conductivity prediction of fruits and vegetables using neural networks

Hussain, Mohd Azlan and Rahman, M.S. (1999) Thermal conductivity prediction of fruits and vegetables using neural networks. International Journal of Food Properties, 2 (2). pp. 121-137. ISSN 10942912,

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Artificial neural network was used to predict the thermal conductivity of various fruits and vegetables (apples, pears, corn starch, raisins and potatoes). Neural networks was also used to model the error between the experimental value and that of the theoretical model developed. Two separate networks were used to perform these separate tasks. The optimum configuration of the networks was obtained by trial and error basis using the multilayered approach with the backpropagation and Levenberg-Marquardt Methods used concurrently in the training of the networks. The results showed that the these networks has the ability to model the thermal conductivity as well as to predict the model/experimental error accurately. The networks can then be used as correction factor to the model in a hybrid approach and gave better prediction of thermal conductivity than the model itself.

Item Type: Article
Additional Information: Cited By (since 1996): 10 Export Date: 5 March 2013 Source: Scopus CODEN: IJFPF Language of Original Document: English Correspondence Address: Hussain, Mohamed Azlan; Univ of MalayaMalaysia References: Billings, S., Chen, S., Neural Networks and system identification (1992) IEE Control Engineering Series No. 46, , Institution of Electrical Engineers, UK; Bomio, M., Neural networks and the future of sensory evaluation (1998) Food Technology, 52 (8), pp. 62-63; Casasent, D.A., Sipe, M.A., Schatzki, T.F., Keagy, P.M., Lee, L.C., Neural net classification of X-ray Pistachio nut data (1998) Lebensm.-Wiss. U. Technol., 31, pp. 122-128; Edwards, N.J., J, C., Direct training method for a continuous time nonlinear optimal feedback controller (1995) Journal of Optimization Theory and Application, 84 (3), pp. 509-528; Hussain, M.A., Review of the applications of neural networks in chemical process control-simulation and online implementation (1999) Artificial Intelligence in Engineering, 13 (1), pp. 55-68; Linko, S., Expert systems- What can they do for the food industry? (1998) Trends in Food Science and Technology, 9, pp. 3-12; Moran, A.J., Harstan, C.T., Pap, R.M., (1990) Handbook of Neural Computing Applications, , Academic Press, UK; Morris, A.J., Montague, G.A., Willis, M.J., Artificial Neural Network: Studies in process modeling and control (1994) Trans IChemE-Part A, 72, pp. 3-19; Mujtaba, I.M., Hussain, M.A., Optimal Operation of dynamics process under process model mismatches (1998) Computational Chemical Engineering, 22, pp. S621-624; Rahman, M.S., (1995) Food Properties Handbook, , CRC Press, Boca raton, FL; Rahman, M.S., Chen, X.D., Perera, C.O., An improved thermal conductivity model for fruits and vegetables as a function of temperature, water content and porosity (1997) Journal of Food Engineering, 31, pp. 163-170; Robitalk, B., Marcos, B., Modified quasi-Newton method for training neural network (1996) Computers and Chemical Engineering, 20 (9), pp. 1133-1140; Rumelhart, D.E., McClelland, J.L., (1986) Parallel Distributed Processing, , Chapter 8., MIT Press, Cambridge; Sablani, S.S., Ramaswamy, S.H., Neural Network modeling of heat transfer to liquid particle mixtures in cans subjected to end-over-end processing (1997) Food Research International, 30 (2), pp. 105-116
Uncontrolled Keywords: Backpropagation; Feedforward neural networks; Fruits; Mathematical models; Thermal conductivity of solids; Levenberg-Marquardt method; Vegetables; Food processing.
Subjects: T Technology > TA Engineering (General). Civil engineering (General)
T Technology > TP Chemical technology
Divisions: Faculty of Engineering
Depositing User: Mr Jenal S
Date Deposited: 10 Jul 2013 08:24
Last Modified: 10 Feb 2021 03:27

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