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

Abstract

This study aimed to develop and validate an improved ultrasonic-assisted extraction (UAE) method for rapid and accurate determination of soybean oil content to address the limitations of conventional methods, including time-consuming operation, low efficiency, and unstable detection accuracy. 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 (R2) 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. Further validation across 19 soybean cultivars verified the good universality and stability of the proposed UAE method. Scanning electron microscopy (SEM) characterization further demonstrated that ultrasonic treatment effectively destroyed soybean cell wall structures, thereby promoting oil release and improving extraction efficiency. Compared with conventional detection approaches, the established UAE strategy possesses the prominent advantages of high efficiency, stable accuracy, and strong cultivar adaptability. This study provides a reliable and efficient technical approach for the rapid determination of soybean oil content and offers technical support for soybean quality evaluation and industrial quality control.