Prediction of the germination rate and antioxidant properties of VD20 Rice by utilizing Artificial neural network-coupled response surface methodology and product characterization - Loan, Le Thi Kim; Tat, Truong Quoc; Minh, Pham Do Trang; Thao, Vo Thi Thu; Hoang, Pham Thi Minh; Nhi, Tran Thi Yen; Giang, Bach Long; Phat, Dao Tan; Tai, Ngo Van (My University, 2024)
The current research aims to predict and optimize process conditions to produce germinated VD20 with a high rate of germination and antioxidant properties. Box-Behnken design (BBD) was used to build models with three factors [soaking time (ST: 4–6 h), germination time (GT: 18–22 h), and germination temperature (33–37 °C)] and three replications. The data set from the BBD experiment was used to create an artificial neural network (ANN) model that estimated the change in responses by variable factors. The ANN model was extremely accurate, with an overall correlation coefficient (R) of 0.9997 and showed the best fit with actual and predicted data. The germination conditions were further optimized using multi-objective RSM, which revealed that the optimal conditions were ST of 5.34 h, GT of 20.78 h, and germination temperature of 35.6 °C. The experimental validation revealed a high level of agreement between the results of the BBD models forecasted and the actual experimental values. The ANN-coupled BBD methodology is a promising hybrid method for modeling, forecasting, and optimizing the impact of process conditions on the quality of germinated grain. In addition, the raw and germinated VD20 rice were analyzed for their techno-functional properties, estimated glycemic index (eGI), and FTIR. Lower peak viscosity, values of breakdown and setback, and phytic acid were found after rice was germinated. The result revealed that high antioxidant content and activity, which were confirmed by the FTIR pattern, and low digestion behaviors (eGI = 64.23) were the attributes of the germinated product. Furthermore, the results of pasting, thermal, swelling power, and solubility showed the wide range of further application of this material, which should receive more consideration in future research.
Keywords: Artificial neural network; Characterization; Germination; Modelling; Optimization; Response surface methodology