R语言与正交试验对黄芪中黄芪多糖提取工艺的优化比较
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摘要
目的通过R语言与正交试验分析,优化黄芪中有效成分黄芪多糖的提取工艺。方法采用水提醇沉法,以黄芪多糖提取量为指标,采用四因素(乙醇浓度、提取时间、提取次数及液料比)三水平正交试验设计法分组提取。通过经典正交分析法、R语言结合BP神经网络及遗传算法进一步目标寻优法优化黄芪多糖提取工艺,将两种优化工艺进行对比验证,得到黄芪多糖的最佳提取工艺。结果正交分析法得到黄芪多糖的优化提取工艺为:乙醇浓度75%,提取2 h,提取次数3次,液料比40∶1,此条件下黄芪中黄芪多糖的平均提取量为14.03 mg/g。R语言得到的优化提取工艺为:乙醇浓度95%,提取3 h,提取1次,液料比20∶1,此条件下黄芪中黄芪多糖提取量为18.68 mg/g。最佳工艺验证得出,正交分析优化法RSD为2.27%,R语言处理优化法RSD为1.51%,与预测结果相对误差仅有0.631%。结论 R语言环境下,BP神经网络模型与遗传算法的结合对实验结果具有良好的预测性,为中药材提取及相关制剂工艺优化提供了一种经济高效的优化方法。
Objective: To optimize the extraction process of astragalus polysaccharide from astragalus by analyzing R language and orthogonal test. Methods: Water extraction and ethanol precipitation were used to extract Astragalus polysaccharides,extraction of Astragalus polysaccharide as an index,and four factors(ethanol concentration,extraction time,extraction frequencies and liquid material ratio) and three levels orthogonal design were used. The extraction process of Astragalus polysaccharides was optimized by classical orthogonal analysis,R language combined with BP neural network and genetic algorithm. The two optimization processes were compared and verified,and the optimal extraction process of Astragalus polysaccharides was obtained. Results: The opti mum extraction technology of Astragalus Polysaccharides by orthogonal analysis was 75% ethanol concentration,2 h extraction,3 extraction,liquid-to-material ratio 40∶1. Under these conditions,the average extraction amount of Astragalus Polysaccharides from Astragalus was 14.03 mg/g. The optimum extraction process obtained by R language was 95% ethanol concentration,3 h extraction,1 extraction,liquid-to-material ratio 20∶1. Under these conditions,the average extraction amount of Astragalus Polysaccharides from Astragalus was 18.68 mg/g. RSD of the orthogonal analysis method was 2.27%,which of R language processing method was 1.51%,and the relative error was only 0.631%. Conclusion: Under R language environment,the combination of BP neural network model and genetic algorithm has good predictability to the experimental results,which provides an economical and efficient optimization method for the extraction of Chinese medicinal materials and the optimization of related preparations.
Objective: To optimize the extraction process of astragalus polysaccharide from astragalus by analyzing R language and orthogonal test. Methods: Water extraction and ethanol precipitation were used to extract Astragalus polysaccharides,extraction of Astragalus polysaccharide as an index,and four factors(ethanol concentration,extraction time,extraction frequencies and liquid material ratio) and three levels orthogonal design were used. The extraction process of Astragalus polysaccharides was optimized by classical orthogonal analysis,R language combined with BP neural network and genetic algorithm. The two optimization processes were compared and verified,and the optimal extraction process of Astragalus polysaccharides was obtained. Results: The opti mum extraction technology of Astragalus Polysaccharides by orthogonal analysis was 75% ethanol concentration,2 h extraction,3 extraction,liquid-to-material ratio 40∶1. Under these conditions,the average extraction amount of Astragalus Polysaccharides from Astragalus was 14.03 mg/g. The optimum extraction process obtained by R language was 95% ethanol concentration,3 h extraction,1 extraction,liquid-to-material ratio 20∶1. Under these conditions,the average extraction amount of Astragalus Polysaccharides from Astragalus was 18.68 mg/g. RSD of the orthogonal analysis method was 2.27%,which of R language processing method was 1.51%,and the relative error was only 0.631%. Conclusion: Under R language environment,the combination of BP neural network model and genetic algorithm has good predictability to the experimental results,which provides an economical and efficient optimization method for the extraction of Chinese medicinal materials and the optimization of related preparations.
引文
[1]国家药典委员会.中华人民共和国药典(一部)[M].北京:中国中医药出版社,2015:302-303.
[2]柏冬志,东方,唐文婷,等.黄芪多糖药理作用的研究进展[J].黑龙江医药,2014,27(1):103-106.
[3]李红法,郭松波,满淑丽,等.乙醇分级沉淀提取黄芪多糖及其理化性质和抗氧化活性研究[J].中国中药杂志,2015,40(11):2112-2116.
[4] Xue H,Gan F,Zhang Z,et al. Astragalus polysaccharides inhibits PCV2 replication by inhibiting oxidative stress and blocking NF-κB pathway[J]. International Journal of Biological Macromolecules,2015,81(7):22-23.
[5] Wang QH,Qiang T,Jing JH,et al. Orthogonal test design for optimization of the isolation and purification of total favonoids from Artemisia frigida willd using macroporous resin chromatography[J]. African Journal of Pharmacy&Pharmacology,2016, 10(11):192-199.
[6]郭会青,于俊美,田勤.黄芪粗多糖提取工艺的优化[J].食品研究与开发,2015,36(21):78-80.
[7] Paradise E,Claude J,Strimmer K. APE:Analyses of Phylogenetics and Evolution in R language[J]. Bioinformatics,2004,20(2):289-290.
[8] Gulo CASJ, Rúbio TRPM. Text mining scientific articles using the R language[C]. Doctoral Symposium in Informatics Engineering, DOI:10.13140/RG.2.1.3676.3363.
[9] Kotthaus H,Korb I,Engel M, et al. Dynamic page sharing optimization for the R language[J]. Acm Sigplan Notices,2014,50(2):79-90.
[10]虞立,张宇燕,杨洁红,等. R语言多目标优化养阴通脑颗粒组方中3种黄酮类指标成分提取工艺[J].中草药,2015,46(8):1156-1160.
[11]陈茜茜,金伟锋,熊耀康,等. R语言优化温郁金油的羟丙基-β-环糊精包合工艺[J].中华中医药杂志,2016,31(4):1460-1463.
[12] Xiao Z,Ye SJ,Zhong B,et al. BP neural network with rough set for short term load forecasting[J]. Expert Systems with Applications An International Journal,2009,36(1):273-279.
[13] Jin W,Li ZJ,Wei LS,et al. The improvements of BP neural network learning algorithm[C]. Signal Processing Proceedings,2000. WCCC-ICSP 2000. 5th International Conference on. IEEE.
[14]周苏娟,赵斌,孟江,等.基于BP神经网络和遗传算法优化茺蔚子水苏碱提取工艺的研究[J].广州中医药大学学报,2015,32(4):735-738.
[15] Li JP,Balazs ME,Parks GT,et al. A species conserving genetic algorithm for multimodal function optimization[J]. Evolutionary Computation,2014,10(3):207-234.
[2]柏冬志,东方,唐文婷,等.黄芪多糖药理作用的研究进展[J].黑龙江医药,2014,27(1):103-106.
[3]李红法,郭松波,满淑丽,等.乙醇分级沉淀提取黄芪多糖及其理化性质和抗氧化活性研究[J].中国中药杂志,2015,40(11):2112-2116.
[4] Xue H,Gan F,Zhang Z,et al. Astragalus polysaccharides inhibits PCV2 replication by inhibiting oxidative stress and blocking NF-κB pathway[J]. International Journal of Biological Macromolecules,2015,81(7):22-23.
[5] Wang QH,Qiang T,Jing JH,et al. Orthogonal test design for optimization of the isolation and purification of total favonoids from Artemisia frigida willd using macroporous resin chromatography[J]. African Journal of Pharmacy&Pharmacology,2016, 10(11):192-199.
[6]郭会青,于俊美,田勤.黄芪粗多糖提取工艺的优化[J].食品研究与开发,2015,36(21):78-80.
[7] Paradise E,Claude J,Strimmer K. APE:Analyses of Phylogenetics and Evolution in R language[J]. Bioinformatics,2004,20(2):289-290.
[8] Gulo CASJ, Rúbio TRPM. Text mining scientific articles using the R language[C]. Doctoral Symposium in Informatics Engineering, DOI:10.13140/RG.2.1.3676.3363.
[9] Kotthaus H,Korb I,Engel M, et al. Dynamic page sharing optimization for the R language[J]. Acm Sigplan Notices,2014,50(2):79-90.
[10]虞立,张宇燕,杨洁红,等. R语言多目标优化养阴通脑颗粒组方中3种黄酮类指标成分提取工艺[J].中草药,2015,46(8):1156-1160.
[11]陈茜茜,金伟锋,熊耀康,等. R语言优化温郁金油的羟丙基-β-环糊精包合工艺[J].中华中医药杂志,2016,31(4):1460-1463.
[12] Xiao Z,Ye SJ,Zhong B,et al. BP neural network with rough set for short term load forecasting[J]. Expert Systems with Applications An International Journal,2009,36(1):273-279.
[13] Jin W,Li ZJ,Wei LS,et al. The improvements of BP neural network learning algorithm[C]. Signal Processing Proceedings,2000. WCCC-ICSP 2000. 5th International Conference on. IEEE.
[14]周苏娟,赵斌,孟江,等.基于BP神经网络和遗传算法优化茺蔚子水苏碱提取工艺的研究[J].广州中医药大学学报,2015,32(4):735-738.
[15] Li JP,Balazs ME,Parks GT,et al. A species conserving genetic algorithm for multimodal function optimization[J]. Evolutionary Computation,2014,10(3):207-234.