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星点设计-效应面法优选小儿益肺健脾煎膏剂提取工艺研究 被引量:2

Optimization of extraction technology for Xiao’er Yifei Jianpi Paste by central composite design-response surface methodology
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摘要 目的采用星点设计-响应面法优选小儿益肺健脾煎膏剂提取工艺。方法以加水倍数、提取时间为考察因素,橙皮苷含量与干膏率的总评"归一值"为考察指标,进行二因素五水平试验,并分别作多元线性回归及二项式拟合,用所得效应面对结果进行预测分析。结果最佳提取工艺条件为:21倍量水,煎煮2次,每次212 min,总评"归一值"为0.836 30,与预测值相比较,偏差率为-16.531%(在-20.000%内)。结论优选的提取工艺简便、稳定,具有较好、可靠的预测性。 Objective To optimize the extraction technology for Xiao’er Yifei Jianpi Paste by central composite designresponse surface methodology. Methods Based on taking water volume and extraction time as investigation factors,overall rating of "normalized value" of dry extract yield and the content of Hesperidin as evaluation index, the two-factor and five-level test was carried out. The experimental data were fitted by multiple linear regression and binomial fitting, and the obtained response surface were used to predict the results. Results The optimal extraction technology was as follows: 21-folds of water, extracted twice, 212 min per time. The overall rating of "normalized value" was 0.836 30,compared with the predicted value, the deviation rate was-16.531%(which was within-20.000%). Conclusion The optimal extraction process is simple, stable and has good and reliable predictability.
作者 韦艳萍 谭栀恩 陀海燕 曾佩琴 刘雅婷 覃柳馨 易蔚 陈卫卫 WEI Yanping;TAN Zhi’en;TUO Haiyan;ZENG Peiqin;LIU Yating;QIN Liuxin;YI Wei;CHEN Weiwei(College of Pharmacy,Guangxi University of Chinese Medicine,Guangxi Zhuang Autonomous Region,Nanning 530001,China;Guangxi Key Laboratory of Common Technology of Traditional Chinese Medicine Preparation in Laboratory,Guangxi Zhuang Autonomous Region,Nanning 530001,China;Guangxi Zhuang Yao Key Laboratory of Medicine,Guangxi Zhuang Autonomous Region,Nanning 530001,China)
出处 《中国医药导报》 CAS 2021年第1期13-17,35,F0003,共7页 China Medical Herald
基金 广西中药制剂共性技术研发重点实验室课题(70-ZJGX2017006) 广西壮瑶药重点实验室(GXZYZZ2019-13) 广西中医药大学引进博士科研启动基金项目(2018BS029)。
关键词 小儿益肺健脾煎膏剂 橙皮苷 提取工艺 星点设计-效应面法 Xiao’er Yifei Jianpi Paste Hesperidin Extraction technology Central composite design-response surface methodology
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