Predictions of viscoelastic behavior of pomegranate using artificial neural network and Maxwell model

Abstract

Stress relaxation is one of the defined tests to characterize the viscoelastic properties of food and agricultural materials. Stress relaxation data are very important because they provide useful and valuable information such as fruit firmness and ripening, food processing and predicting changes in the material during mechanical loading. Viscoelastic behavior of some varieties of pomegranate that are cultivated in Iran has been studied in current research. For this purpose, stress relaxation test was conducted with three cultivars of pomegranate (Ardestani, Shishekap and Malas) for three sizes (small, medium and large). In this article the potential of artificial neural network (ANN) technique is evaluated as an alternative method for Maxwell model to predict the viscoelastic behavior of pomegranate. Neural stress relaxation models were constructed to describe stress relaxation behavior of pomegranate with respect to time. The neural models were built based upon relaxation time as input network and stress relaxation as output network. The results revealed that both ANN model and Maxwell model have high capability of producing accurate and reliable predictions for stress.