Neural networks for predicting thermal conductivity of bakery products
2002
Sablani, S.S. | Baik, O.D. | Marcotte, M.
An artificial neural network (ANN) approach was used to model the thermal conductivity of bakery products as a function of product moisture content, temperature and apparent density. The bakery products considered in this work were bread, bread dough, French bread, yellow cake, tortilla chip, whole wheat dough, baked chapati and cup cake. Data on thermal conductivity of bakery products were obtained from the literature for a wide range of product moisture contents, temperatures and apparent densities resulted from different baking conditions. In developing the ANN model, several configurations were evaluated. The optimal ANN model was found to be a network with six neurons in each of the two hidden layers. This optimal model was capable of predicting the thermal conductivity values of various bakery products for a wide range of conditions with a mean relative error of 10%, a mean absolute error of less than 0.02 W/m K and a standard error of about 0.003 W/m K. The simplest ANN model, which had one hidden layer and two neurons, predicted thermal conductivity values with a mean relative error of less than 15%.
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