PDF
Full
Outline
Objective To optimize the ultrasonic extraction process of dihydromyricetin from Ampelopsis grossedentata using artificial neural networks combined with response surface methodology. Methods The stems and leaves of Ampelopsis grossedentata were used as the research material. An ultrasonic extraction system for dihydromyricetin was established, and the process parameters were systematically optimized using a combination of single-factor experiments, response surface methodology and artificial neural network models optimized by genetic algorithms. The extraction yields of dihydromyricetin from different parts of Ampelopsis grossedentata were then analyzed under optimal conditions. Results The artificial neural network model exhibited superior accuracy and predictive capability in comparison to the response surface methodology. The optimal extraction conditions were determined to be an ultrasonic power of 360 W, a temperature of 42 ℃, a liquid-to-solid ratio of 20:1 (mL:g), and an extraction time of 35 min. Under these conditions, the actual extraction yield of dihydromyricetin was (39.83±0.01)%, with a relative error of only 0.36% compared to the artificial neural network-predicted value of 40.19%. Furthermore, the extraction yield of dihydromyricetin from various parts of Ampelopsis grossedentata under optimal ultrasonic conditions followed the sequence: Stems and leaves of Ampelopsis grossedentata>branches of Ampelopsis grossedentata>pruned branches of Ampelopsis grossedentata. Conclusion This study successfully optimizes the ultrasonic extraction process to enhance the extraction efficiency of dihydromyricetin from Ampelopsis grossedentata and reveals significant differences in dihydromyricetin extraction yields among different parts of the plant.
PDF
271
119
| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
关闭全屏
No. 86 Xueyuan South Road, Haidian District, Beijing
010-62199257
qkjq@cast.org.cn
Copyright © 2025 China Association for Science and Technology. All rights reserved. For all open access content, the relevant licensing terms apply.
Sponsored by the Office of the Leading Group for Cybersecurity and Informatization of CAST, and supported by Science and Technology Review Publishing House
