Prediction of extrudate properties using artificial neural networks

A backpropagation artificial neural network (ANN) model was developed to predict the properties of extrudates generated by extrusion cooking of fish muscle-rice flour blend in a single screw extruder. Experimental data obtained in a previous study on extrudate properties of expansion ratio, bulk density and hardness at different combinations of operating variables of barrel temperature, feed content and feed moisture had been analysed using response surface methodology (RSM). A backpropagation neural network model was implemented in MATLAB and was trained for operating variables (inputs) and for each individual measured extrudate properties expansion ratio ER, bulk density BID and harndess H (outputs). The optimized network indicated that one hidden layer with a learning rate of 0.1, steep descent learning rule, 100 000 epochs and a logistic sigmoid transfer function predicted the extrudate properties better than RSM. The agreement of the ANN model with the experimental values, expressed as sum of squared error values, was 9.8 x 10(-7) for ER, 5.8 x 10(-2) for BD and 3.8 x 10(-3) for H. The ANN prediction for the optimized process conditions was superior to the RSM values, with percentage errors of +6.06% (ER), +4.08% (BD) and -14.28% (H).