Box-Behnken响应面法优选樟帮酒润麸炒桑枝最佳炮制工艺
Optimal processing technology of Zhangbang wine moistening bran stir-fried Mori Ramulus through Box-Behnken response surface method
刘雅琪 1周彦如 1谢作桦 2周立翔 2盛文胜2
作者信息
- 1. 江西德上医药研究院有限公司,樟树 331200;江西中医药大学药学院,南昌 330004
- 2. 江西德上医药研究院有限公司,樟树 331200
- 折叠
摘要
目的 优选樟帮酒润麸炒桑枝最佳炮制工艺.方法 以桑皮苷A、绿原酸、白藜芦醇、白藜芦醇苷、氧化白藜芦醇、桑色素、桑酮G、桑辛素8个成分总含量为考查指标,对樟帮酒润麸炒桑枝中麦麸用量、黄酒用量、炒制温度、浸润时间、炮制时间5个影响因素进行单因素考察,运用Box-Behnken响应面法进行试验设计,优选酒润麸炒桑枝最佳炮制工艺.结果 响应面法优选樟帮酒润麸炒桑枝最佳炮制工艺为浸润时间116 min,麦麸用量19％,黄酒用量10％,炒制时间7 min,炒制温度237 ℃,该工艺条件下,桑枝饮片中8个成分平均总含量为787.8 μg/g,RSD值为0.98％.结论 优选的樟帮酒润麸炒桑枝最佳炮制工艺稳定可靠、重复性好,可更好保障樟帮酒润麸炒桑枝质量.
Abstract
Objective To optimize the technology of Zhangbang wine moistening bran stir-fried Mori Ramulus.Methods Using the total contents of 8 components including mulberry bark glycoside A,chlorogenic acid,resveratrol,resveratrol glycoside,oxidized resveratrol,mulberry pigment,mulberry ketone G,and mulberry saponin as the examination indicators,single factor investigation of five influencing factors of Zhangbang wine moistening bran stir-fried Mori Ramulus:wheat bran dosage,yellow wine dosage,frying temperature,soaking time,and processing time was conducted.Box Behnken response surface methodology was used for experimental design,to optimize processing technology for Zhangbang wine moistening bran stir-fried Mori Ramulus.Results The optimal processing technology of the response surface method was:soaking time 116 min,wheat bran amount 19％,rice wine amount 10％,roasting time 7 min,and roasting temperature 237 ℃.Under these conditions,the average total content of eight components in the prepared Mori Ramulus decoction pieces was 787.8 μg/g,and RSD was 0.98％.Conclusion The optimal processing technology of Zhangbang wine moistening bran stir-fried Mori Ramulus is stable,reliable and repeatable,which can better guarantee the quality of Zhangbang wine moistening bran stir-fried Mori Ramulus.
关键词
桑枝/Box-Behnken响应面法/樟帮/酒润麸炒/炮制(中药)Key words
Mori Ramulus/Box-Behnken response surface method/Zhangbang/Wine moistening bran stir-fry/Processing(TCD)引用本文复制引用
基金项目
江西省中医药管理局科技计划课题(2020Z010)
出版年
2024
NETL
NSTL
万方数据