Thermal conductivity prediction of fruits and vegetables using neural networks

Mohamed Azlan Hussain, M. Shafiur Rahman

Research output: Contribution to journalArticle

20 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)121-137
Number of pages17
JournalInternational Journal of Food Properties
Volume2
Issue number2
Publication statusPublished - 1999

Fingerprint

Thermal Conductivity
thermal conductivity
Vegetables
neural networks
Fruit
vegetables
fruits
prediction
Theoretical Models
Pyrus
Vitis
Malus
Solanum tuberosum
Starch
Zea mays
raisins
corn starch
pears
apples
potatoes

ASJC Scopus subject areas

  • Food Science

Cite this

Thermal conductivity prediction of fruits and vegetables using neural networks. / Hussain, Mohamed Azlan; Rahman, M. Shafiur.

In: International Journal of Food Properties, Vol. 2, No. 2, 1999, p. 121-137.

Research output: Contribution to journalArticle

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