Artificial neural network for the prediction of temperature, moisture and fat contents in meatballs during deep-fat frying

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*Correspondent: Fax: 519 836 0227; e-mail: gmittal@uoguelph.ca

Abstract

An artificial neural network (ANN) was developed to predict heat and mass transfer during deep-fat frying of meatballs. Frying time, radius of meatball, fat diffusivity, moisture diffusivity, heat transfer coefficient, fat conductivity, initial moisture content, thermal diffusivity, initial meatball temperature and oil temperature were all input variables. Temperature at meatball geometrical centre (T0), average temperature of meatball (Tave), average fat content of meatball (mf,ave), and average moisture content of meatball (mave) were outputs. The data used to train and verify the ANN were obtained from validated mathematical models. Trained ANN predicted T0, Tave, mf,ave and mave with 0.54, 0.14, 0.03 and 0.10% mean relative errors, respectively.

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