Prediction of the viscosity of clarified fruit juice using artificial neural network: a combined effect of concentration and temperature Original Research Article

An artificial neural network (ANN) model is presented for the prediction of viscosity of fruit juice as a function of concentration and temperature. The fruit juices considered in the present study were orange, peach, pear, malus floribunda and black current. The viscosity data of juices (1.53–3300 mPa s) were obtained from the literature for a wide range of concentration (5–70 °Brix) and temperature (30.7–71.7 °C). Several configurations were evaluated while developing the optimal ANN model. The optimal ANN model consisted of two hidden layers with two neurons in each hidden layer. This model was able to predict viscosity with a mean absolute error of 3.78 mPa s. The performance of the ANN was checked using experimental data. Predicted viscosity using the ANN was proved to be a simple, convenient and accurate method. The model can be incorporated in the heat transfer calculations during fruit processing where concentration and temperature dependent viscosity values are required. This may also be useful in mass transfer calculations during filtration of the juice using membranes for clarification.