中国中医药信息杂志2024，Vol.31Issue(2)：138-143,6.DOI:10.19879/j.cnki.1005-5304.202308082
基于BAS-BP神经网络结合熵权法多指标优化金蕾复方提取工艺
Extraction Process of Jinlei Compound Based on BAS-BP Neural Network Combined with Entropy Weight Method
摘要
Abstract
Objective To optimize the ethanol extraction technology parameters of Jinlei Compound through orthogonal experiment combined with beetle antennae search(BAS)-back propagation(BP)neural network.Methods On the basis of the optimal extraction concentration obtained by single factor investigation,the ratio of solid to liquid,extraction time and extraction times were set as the orthogonal test factors.The entropy weight method was used to calculate the comprehensive scores of the yield of luteolin,kaempferol,swertianin and dry paste.Then,the BAS-BP neural network model was established,and the optimum extraction process was predicted by the BAS.Results BAS-BP neural network optimized Jinlei Compound alcohol extraction process was as follows:solid-liquid ratio 1:10,extraction time of 0.5 h,extraction times of 3,the comprehensive score was 96.352 6.The optimal process parameters obtained by orthogonal design were:solid-liquid ratio 1:10,extraction for 0.5 h,extraction for 3 times,the comprehensive score 90.988 0.The comprehensive score of BAS-BP neural network model was slightly better than that of orthogonal experiment,but the difference between the two was small.The optimal extraction process of Jinlei Compound was determined by comprehensive production practice as the ratio of solid to liquid 1:10,extraction for 0.5 h,extraction for 3 times.Conclusion The optimized process based on BAS-BP neural network has higher extraction efficiency and good stability,which can provide reference for subsequent development and quality control.关键词
金蕾复方/正交设计/BP神经网络/天牛须搜索算法/熵权法/多指标综合评分法Key words
Jinlei Compound/orthogonal design/BP neural network/beetle antennae search/entropy weight method/multi-index comprehensive scoring method分类
医药卫生引用本文复制引用
王嘉鸣,柳娜,陈晖,景明..基于BAS-BP神经网络结合熵权法多指标优化金蕾复方提取工艺[J].中国中医药信息杂志,2024,31(2):138-143,6.基金项目
国家自然科学基金(82160854) （82160854）
甘肃省教育科技创新项目(2021CYZC-13) （2021CYZC-13）
