基于D-S证据理论的数学模型优化补阳还五汤中多目标有效成分提取工艺
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摘要
目的采用新颖数学模型优化补阳还五汤中羟基红花黄色素A、阿魏酸、芒柄花素3种有效成分的提取工艺条件。方法采用响应面实验设计方法对补阳还五汤中羟基红花黄色素A、阿魏酸、芒柄花素3种成分进行提取,高效液相色谱法进行含量测定,计算各自提取率。利用D-S证据理论对3种成分提取率进行归一化,得到综合评价值,再运用遗传-神经网络模型和响应面方法,对综合评价值及相应的提取工艺条件进行目标寻优和预测。结果遗传-神经网络模型优化的最佳提取条件为:提取时间1.7 h、乙醇浓度50%、提取温度90℃、液料比16∶1,在此条件下寻优得到的综合评价值为77.20,平均验证真实值为76.47,两者RSD为1.49%。响应面法优化的最佳提取条件为:提取时间1.7 h,乙醇浓度53%,提取温度91℃,液料比14∶1,在此条件下寻优得到的响应面结果为66.75,平均验证真实值为70.82,两者RSD为2.64%。上述结果可知,响应面法的综合评价值预测结果较遗传神经网络模型低,且低于30组实验中的第3组,故寻优结果较劣;响应面法的验证试验真实值也较差于遗传神经网络模型的,故D-S证据理论下的遗传神经网络模型优于响应面法。结论将D-S证据理论与遗传神经网络模型结合应用于补阳还五汤有效成分多目标提取工艺优化、分析和预测,预测与验证结果吻合度较高,表明该方法可行性较好,这对中药药效物质基础的提取工艺优化研究提供了新的思路与参考。
Objective: To optimize the extraction process of hydroxy safflower yellow A,ferulic acid and formononetin in Buyang Huanwu Decoction with novel mathematical model. Methods: The response surface experiment design method was adopted to extract three active ingredients in Buyang Huanwu Decoction. Three active ingredients of the extract were determined by high performance liquid chromatography,and the result was expressed by extraction rate. The D-S evidence theory was used to uniformize the three ingredients extraction rate and get the comprehensive evaluation value finally. Through the genetic neural network model and the response surface analysis,target optimization and prediction were carried out on the comprehensive evaluation value and corresponding extraction process conditions. Results: The best extraction process of genetic neural network model were as follows: extraction time was 1.7 h,ethanol concentration 50%,extraction temperature 90 ℃,liquid-to-solid ratio 16∶1. Under this condition,the prediction value was 77.20,and the average of the verification test was76.47. The RSD of the above two results was 1.49 %. The optimum extraction technology in the response surface analysis were as follows: extraction time was 1.7 h,ethanol concentration 53%,extraction temperature 91 ℃,liquid material ratio 14∶1. Under this condition,the predicted value was 66.75,and the average of the verification test was 70.82. The RSD of the above two results was 2.64%. From the above results,the comprehensive evaluation value of the response method was smaller than that of the genetic neural network model,and less than the third in 30-group experiments. Therefore,the result of optimization was inferior. The final average comprehensive evaluation value of the verification experiment of the response method was also smaller than the genetic neural network model. Hence,the genetic neural network model based on D-S evidence theory was superior to the response surface method. Conclusion: D-S evidence theory combined with genetic neural network model was suitable to optimize the extraction process of the main active ingredients in Buyang Huanwu Decoction,with better feasibility,providing a new idea and reference for optimizing the extraction process of the effective substance base of Chinese medicine.
Objective: To optimize the extraction process of hydroxy safflower yellow A,ferulic acid and formononetin in Buyang Huanwu Decoction with novel mathematical model. Methods: The response surface experiment design method was adopted to extract three active ingredients in Buyang Huanwu Decoction. Three active ingredients of the extract were determined by high performance liquid chromatography,and the result was expressed by extraction rate. The D-S evidence theory was used to uniformize the three ingredients extraction rate and get the comprehensive evaluation value finally. Through the genetic neural network model and the response surface analysis,target optimization and prediction were carried out on the comprehensive evaluation value and corresponding extraction process conditions. Results: The best extraction process of genetic neural network model were as follows: extraction time was 1.7 h,ethanol concentration 50%,extraction temperature 90 ℃,liquid-to-solid ratio 16∶1. Under this condition,the prediction value was 77.20,and the average of the verification test was76.47. The RSD of the above two results was 1.49 %. The optimum extraction technology in the response surface analysis were as follows: extraction time was 1.7 h,ethanol concentration 53%,extraction temperature 91 ℃,liquid material ratio 14∶1. Under this condition,the predicted value was 66.75,and the average of the verification test was 70.82. The RSD of the above two results was 2.64%. From the above results,the comprehensive evaluation value of the response method was smaller than that of the genetic neural network model,and less than the third in 30-group experiments. Therefore,the result of optimization was inferior. The final average comprehensive evaluation value of the verification experiment of the response method was also smaller than the genetic neural network model. Hence,the genetic neural network model based on D-S evidence theory was superior to the response surface method. Conclusion: D-S evidence theory combined with genetic neural network model was suitable to optimize the extraction process of the main active ingredients in Buyang Huanwu Decoction,with better feasibility,providing a new idea and reference for optimizing the extraction process of the effective substance base of Chinese medicine.
引文
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[14]梁茂宗,周登极,张会生,等.基于改进D-S证据理论的燃气轮机滑油系统故障诊断[J].燃气轮机技术,2018,31(2):17-22,36.
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[3]王宏涛,黄志良.补阳还五汤加减治疗缺血性中风(气虚血瘀证)8例[J].河南中医,2008,28(8):83-84.
[4]董婷,杨文明,汪美霞,等.补阳还五汤治疗急性脑梗死并发脑心综合征(气虚血瘀证)90例临床观察[J].中国中医急症,2011,20(1):5-7.
[5]周曾璇,陈雪梅,易健,等.补阳还五汤治疗脑卒中后抑郁的疗效和安全性系统评价[J].中国中医急症,2015,24(7):1142-1143.
[6]尉建辉,张建军,张会平,等.补阳还五汤对急性脑梗死患者神经功能及心脏血清学指标的影响[J].中国中医急症,2016,25(11):2136-2138.
[7]国家药典委员会.中华人民共和国药典(一部)[M].北京:中国医药科技出版社,2015:41,151,302.
[8]Wei X,Liu H,Sun X,et al.Hydroxysafflor yellow A protects rat brains against ischemia-reperfusion injury by antioxidant action[J].Neuroscience Letters,2005,386(1):58-62.
[9]Mancuso C,Santangelo R.Ferulic acid:pharmacological and toxicological aspects[J].Food&Chemical Toxicology An International Journal Published for the British Industrial Biological Research Association,2014,65(2):185-195.
[10]张宝成,吴剑,秦亚东,等.三维同步荧光光谱法测定黄芪中芒柄花素的含量[J].中药新药与临床药理,2018,29(1):67-70.
[11]Mu H,Bai YH,Wang ST,et al.Research on antioxidant effects and estrogenic effect of formononetin from Trifolium pratense(red clover)[J].Phytomedicine International Journal of Phytotherapy&Phytopharmacology,2009,16(4):314-319.
[12]Cao H,Sun H,Jia Q,et al.Laser-scanner grid map building based on Dempster-Shafer evidence theory[C].International Conference on Mechatronic&Automation,2009:4930-4935.
[13]Yang J,Huang HZ,Miao Q,et al.A novel information fusion method based on Dempster-Shafer evidence theory for conflict resolution[J].Intelligent Data Analysis,2011,15(3):399-411.
[14]梁茂宗,周登极,张会生,等.基于改进D-S证据理论的燃气轮机滑油系统故障诊断[J].燃气轮机技术,2018,31(2):17-22,36.
[15]周明,孙树栋.遗传算法原理及应用[M].北京:国防工业出版社,1999.
[16]阎平凡,张长水.人工神经网络与模拟进化计算[M].北京:清华大学出版社,2000.