Application of BP artificial neural network in extruded surimi product
A back propagation(BP) artificial neural network(ANN) model was developed to predict the properties of extruded surimi products produced by a twin screw extruder.A BP-ANN model was established in MATLAB to simulate the relationships between running parameters of contents of surimi,screw speed and heating temperature of barrel III with the properties of surimi products such as water holding capability(WHC),swelling degree(SD),hardness(H) and springiness(S) during extrusion process.Using the experimental data from a quadratic general rotary unitized design,the neural network was trained and then validated with a validation subset.Besides,the method of response surface method(RSM) was also used to analyze and predict these properties.By comparing with mean squared error(MSE) and sum squared error(SSE) of BP-ANN and RSM models,it was showed that BP-ANN was more accurate than RSM in predicting the relationship between the responses and the running parameters.The BP-ANN was then used to search for a combination of running parameters resulting in maximal WHC,SD,S and minimal H.As a result,the optimal running parameters for content of surimi,screw speed and heating temperature was 45.70%,170 r/min and 106.2 ℃,respectively.