Development of an Improved Ultrasonic-Assisted Extraction Method for Determining Soybean Oil Content

Two typical soybean varieties (Jinda 117 and Jinda 114) were selected as research materials. On the basis of single-factor experiments and thermogravimetric analysis, response surface methodology (RSM) and a genetic algorithm-optimized artificial neural network (ANN-GA) were employed to model and optimize the UAE extraction process.

The predictive performance and reliability of the RSM and ANN-GA models were systematically validated by coefficient of determination (<i>R</i>²) and absolute average deviation (AAD). Analysis of the experimental dataset demonstrated that the ANN-GA model exhibited higher predictive accuracy and better fitting performance. Under the optimal conditions predicted by the ANN-GA model, the oil yields of Jinda 117 and Jinda 114 reached 22.17% ± 0.23% and 20.91% ± 0.70%, respectively, which were significantly higher than those obtained by RSM optimization, traditional Soxhlet extraction, and maceration methods.