Thermal conductivity prediction of fruits and vegetables using neural networks

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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.