摘要
目的:选取乙醇超声提取与乙醇浸提两种方法,用正交实验结果进行对比选取最优提取方法,利用人工神经网络遗传算法优化得到刺糖低聚糖最佳提取工艺。方法:以乙醇超声波辅助提取与乙醇浸提两种方法单因素实验为基本依据,浸出液中低聚糖含量为指标,采用L;(3;)正交表进行正交实验筛选出待选提取方法及工艺参数,在此基础上建立人工神经网络(artificial neural network)模型,结合遗传算法(genetic algorithm)进行极值寻优,以期获得刺糖低聚糖最佳的提取工艺。结果:所得刺糖低聚糖最佳提取条件为提取温度为85℃、提取时间为3h、液料比1∶40、乙醇浓度为15%,提取率为26.17%。结论:通过人工神经网络-遗传算法得到的提取工艺显著提高了刺糖低聚糖的提取率,本研究为刺糖低聚糖的高效提取提供一定的理论基础与指导意义。
Objective:To optimize extraction process of Saccharum alhagi polysaccharides by artificial neural network and genetic algorithm(ANN-GA).Methods:Using content of Saccharum alhagi polysaccharides in leaching solution as the index,based on the single factor experiment of ethanolultrasonic-assisted extraction and ethanol extraction,an artificial neural network model was established on the basis of L;(3~4)orthogonal test.Genetic algorithm optimization extreme was combined to determine the optimum extraction process.Results:The optimum process parameters for Saccharum alhagi polysaccharides extraction were as follows:extraction temperature 85℃,extraction time 3h,solid to liquid ratio 1∶40,and ethanol concentration 15%.The polysaccharides extraction rate was 26.17%under these conditions.Conclusion:ANN-GA method optimized extraction process,which increased extraction rate of Saccharum alhagi polysaccharides.This study provides a theoretical basis for further study of Saccharum alhagi polysaccharides high performance extraction.
作者
宋建忠
陈盈盈
杨婧
李杰
陈章浩
常军民
SONG Jianzhong;CHEN Yingying;YANG Jing;LI Jie;CHEN Zhanghao;CHANG Junmin(School of Pharmacy,Xinjiang Medical University,Urumqi 830000;Department of Pharmacy,Affiliated Tumor Hospital of Xinjiang Medical University,Urumqi 830011;The 63650 Troop Hospital of the Chinese People’s Liberation Army,Urumqi 830000)
出处
《中国食品添加剂》
CAS
北大核心
2022年第6期1-7,共7页
China Food Additives
基金
国家自然科学基金(82060756)。
关键词
人工神经网络-遗传算法
低聚糖
正交实验
乙醇超声波
artificial neural network-genetic algorithm
oligosaccharide
orthogonal experiment
ethanol ultrasonic wave
作者简介
宋建忠(1988-),男,博士,研究方向:药物分析及医院药学。E-mail:772394661@qq.com;通信作者:常军民(1965-),男,博士,教授,博士生导师,研究方向:天然药物物质基础研究。E-mail:1617265908@qq.com。
