BP神经网络算法多指标优化酸枣仁汤提取工艺
Optimization of the extraction process of Suanzaoren decoction by BP neural network algorithm through multiple indicators
李若暄 1何千千 1刘宏博 1汪子皓 1王艳艳1
作者信息
- 1. 黑龙江中医药大学药学院,哈尔滨 150040
- 折叠
摘要
为了优化酸枣仁汤的提取工艺,以提取时间、提取次数、料液比为考察因素,以总黄酮、总皂苷、总酚、多糖的提取率为评价指标,采用熵权法进行综合评价.在单因素试验的基础上,运用Box-Behnken响应面设计和BP神经网络算法,优化酸枣仁汤化学成分的提取工艺,并进行工艺验证.结果表明,BP神经网络算法预测的最优提取工艺综合评分为149.11,优于Box-Behnken响应面法的综合评分137.16.确定BP神经网络验证的工艺为最优工艺,即提取时间80 min,提取次数2次,料液比1∶7 g/mL,该条件下获得的总黄酮、总皂苷、总酚、多糖含量分别为(10.73±0.63)mg/g,(73.34±1.77)mg/g,(16.73±0.56)mg/g,(413.08±8.34)mg/g.研究为酸枣仁汤的提取工艺优化提供依据.
Abstract
In order to optimize the extraction process of Suanzaoren decoction,the extraction time,extraction times and solid-liquid ratio were taken as the investigation factors,and the extraction rate of total flavonoids,total saponins,total phenols and polysaccharides were taken as the evaluation index,and the entropy weight method was used for comprehensive evaluation.On the basis of single factor experiment,Box-Behnken response surface design and BP neural network were used to optimize the extraction process of the chemical components of Suanzaoren decoction,and the process verification was carried out.The results show that the comprehensive score of the optimal extraction process predicted by BP neural network algorithm is 149.11,which is better than 137.16 of Box-Behnken response surface.The optimal extraction process verified by BP neural network is determined as follows:extraction time 80 min,extraction times 2 times,solid-liquid ratio 1∶7 g/mL.The extraction rates of total flavonoids,total saponins,total phenols and polysaccharides are(10.73±0.63)mg/g,(73.34±1.77)mg/g,(16.73±0.56)mg/g,(413.08±8.34)mg/g,respectively.The study provides a basis for the optimization of the extraction process of Suanzaoren Decoction.
关键词
酸枣仁汤/Box-Behnken响应面/BP-神经网络遗传算法/提取工艺Key words
suanzaoren decoction/Box-Behnken response surface/BP-neural network genetic algorithm/extraction technology引用本文复制引用
基金项目
国家自然科学基金项目(82003974)
黑龙江省自然科学基金项目(LH2022H086)
国家自然科学基金项目匹配项目(2020PT01)
黑龙江中医药管理局项目(ZHY2023-003)
黑龙江中医药大学实验中心项目(KY2022-11)
出版年
2024
国家科技期刊平台
NSTL
万方数据