The Study Of Physicochemical Properties And Quality Prediction Model Of Corn/Ginger-Based Extrudate

The nutritional extrudate molding was studied using a DS32-II twin-screw extrusion with the blend of the powder of corn and ginger. Comparing the content of raw material compositions before and after extrusion, the extrusion processing had significant effects on the water retention index and the content of starch, protein, fat, reducing sugar and soluble fiber of the material.The bulk density, hardness and antioxidant activity (AOA)were used as response value, and an response surface method (RSM) was used to develop the quadratic regression equation between the response values and extrusion parameters including extrusion temperature, screw speed and material moisture. The Multi-Objective optimization was conducted by weighting method and genetic algorithm (GA) based on RSM. The result from GA was superior to weighting method, and the final extrusion parameters were determined as follows:extrusion temperature of 153℃, screw speed of 155 r/min and material moisture of 13%. The quality of extrudates during a long-term storage was significantly affected by storage period and storage temperature. The artificial neuron network (ANN) had a better prediction performance than polynomial model. The best ANN structure (ANNs) for predicting hardness is one hidden layer with ten neurons, and the best ANNs for predicting both of crispness and AOA value is two hidden layers with ten neurons per layer. A high linear correlation was also found between AOA value and hardness and between AOA value and crispness (R2>0.913, R2>0.952). The ANN model was built to predict AOA using hardness and crispness as input values, and the result showed that the optimized ANNs could predict the AOA value from hardness (R2>0.999) consisting of two hidden layers with ten neurons per layer, while optimized ANNs for the prediction of AOA value from crispness (R2>0.993) consisted of two hidden layers with eight neurons per layer.