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基于灰色关联分析法辨识中药生产过程关键工艺参数
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  • 英文篇名:Identification of key process parameters of Chinese materia medica production process based on grey relation analysis
  • 作者:陈勇 ; 陈明 ; 王钧 ; 李页瑞 ; 张欣 ; 徐芳芳 ; 肖伟 ; 吴永江
  • 英文作者:CHEN Yong;CHEN Ming;WANG Jun;LI Ye-rui;ZHANG Xin;XU Fang-fang;XIAO Wei;WU Yong-jiang;College of Pharmaceutical Sciences, Zhejiang University;Suzhou ZeDaXingBang Pharmaceutical Co., Ltd.;Jiangsu Kanion Pharmaceutical Co., Ltd.;
  • 关键词:灰色关联分析法 ; 关键工艺参数 ; 中药生产过程 ; 层次分析法 ; Spearman等级相关分析法 ; 萃取工段 ; 热毒宁注射液
  • 英文关键词:grey relation analysis;;key process parameters;;production process of CMM;;analytic hierarchy process;;Spearman rank correlation analysis;;extraction section;;Reduning Injection
  • 中文刊名:ZCYO
  • 英文刊名:Chinese Traditional and Herbal Drugs
  • 机构:浙江大学药学院;苏州泽达兴邦医药科技有限公司;江苏康缘药业股份有限公司;
  • 出版日期:2019-02-12
  • 出版单位:中草药
  • 年:2019
  • 期:v.50;No.638
  • 基金:现代中药创新集群与数字制药技术平台(2013ZX09402203);; 重大新药创制科技重大专项(2018ZX09201010)
  • 语种:中文;
  • 页:ZCYO201903008
  • 页数:6
  • CN:03
  • ISSN:12-1108/R
  • 分类号:45-50
摘要
目的应用灰色关联分析法辨识中药生产过程关键工艺参数。方法以热毒宁注射液生产过程中金银花、青蒿(金青)萃取工段为例,采用灰色关联分析法计算比较该工段各工艺参数对质量指标影响的重要性大小,并结合层次分析法与Spearman等级相关分析法相互验证。结果灰色关联分析法得出工艺参数对质量指标影响重要性的排序为萃取平均体积流量(X4)>调酸后pH值(X2)>萃取浓缩出膏温度(X7)>醇沉浓缩浸膏质量(X1)>盐酸质量(X3)>萃取时间(X6)>体积流量相对标准偏差(X5),与层次分析法得到的工艺参数排序的相关系数为0.893。根据工艺参数影响重要性大小,确定萃取平均体积流量、调酸后pH值、萃取浓缩出膏温度为金青萃取工段关键工艺参数。结论灰色关联分析法用于分析筛选中药生产过程关键工艺参数具有可行性,并能为下一步建立过程预测模型以及在线反馈调控提供理论参考依据。
        Objective To identify the key process parameters of the Chinese materia medica(CMM) production process by using grey relation analysis(GRA) method. Methods Taking Lonicerae Japonicae Flos and Artemisiae Annuae Herba extraction section of Reduning Injection as an example, GRA was adopted to calculate and compare the influence of the process parameters on the quality index. Meanwhile, analytic hierarchy process(AHP) combined with Spearman rank correlation analysis was used to validate mutually. Results According to GRA results, the relative importance of process parameters was ranked as follows: average volume flow rate of extraction(X4) > pH after acid adjustment(X2) > the paste temperature of extracting concentration(X7) > alcohol precipitation concentrated extract weight(X1) > hydrochloric acid weight(X3) > extraction time(X6) > relative standard deviation of flow rate(X5). The correlation coefficient between the order by GRA and that by AHP was 0.893. According to the importance of process parameters, the average volume flow rate of extraction, the pH after acid adjustment, and the paste temperature of extracting concentration were identified as the key process parameters. Conclusion The results obtained in this study show the feasibility of GRA in selecting key process parameters, which can provide theoretical reference for the establishment of prediction model as well as online feedback regulation.
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