2026-10-14T08:56:53Z https://api.figshare.com/v2/oai

oai:figshare.com:article/34025016 2026-09-29T16:06:56Z category_4 category_146 category_21 category_272 category_873 category_39 category_734 category_133 portal_63 item_type_6 month_year_09_2026

Development of an Improved Ultrasonic-Assisted Extraction Method for Determining Soybean Oil Content Xiao-Fang Lu (25137822) Ao-Jie Lu (25137825) Yi-Jie Zhang (713506) Min Sun (115484) Kai-Lin Zhang (25137828) Yan-Ran Qiao (25137831) Zhao-Yang Song (25137834) Fu-Gui Wang (25137837) Ai-Qin Yue (20498420) Jin-Zhong Zhao (20498423) Wei-Jun Du (10511533) Biochemistry Space Science Biotechnology Environmental Sciences not elsewhere classified Chemical Sciences not elsewhere classified Ecology Biological Sciences not elsewhere classified Plant Biology strong cultivar adaptability soybean quality evaluation scanning electron microscopy response surface methodology optimal conditions predicted offers technical support industrial quality control efficient technical approach absolute average deviation 70 %, respectively traditional soxhlet extraction soybean oil content unstable detection accuracy conventional detection approaches better fitting performance uae extraction process improving extraction efficiency experimental dataset demonstrated proposed uae method assisted extraction method assisted extraction oil yields stable accuracy predictive performance conventional methods low efficiency high efficiency ± 0 systematically validated study provides study aimed significantly higher research materials r </ prominent advantages maceration methods jinda 117 jinda 114 including time improved ultrasonic good universality genetic algorithm factor experiments determination (< consuming operation accurate determination 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 (<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. 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. 2026-09-29T00:00:00Z Text Journal contribution 10.1021/acsomega.6c04123.s001 https://figshare.com/articles/journal_contribution/Development_of_an_Improved_Ultrasonic-Assisted_Extraction_Method_for_Determining_Soybean_Oil_Content/34025016 CC BY-NC 4.0