摘要
目的利用近红外光谱分析技术建立注射用益气复脉(冻干)主要原料红参醇提过程中3种单体皂苷——人参皂苷Rg1、Re和Rb1的定量模型,实现提取过程中关键指标的快速检测。方法在线采集红参醇提过程的近红外光谱,以超高效液相色谱(UPLC)法测定提取过程药液中人参皂苷Rg1、Re和Rb1的量为参考值,采用偏最小二乘法建立光谱与测定值之间的定量校正模型,进而对提取过程进行在线分析。结果人参皂苷Rg1和Re的建模波段均为9 403.7~7 498.3 cm^(-1)和6 102~5 446.3 cm^(-1)组合波段;人参皂苷Rb1的建模波段为5 774.1~5 446.3 cm^(-1)。人参皂苷Rg1、Re、Rb1定量模型的交叉验证决定系数(R2)分别为99.40、99.44、99.41,交叉验证均方根误差分别为5.18、2.77、11.00。结论所建立的3种单体皂苷定量模型预测性能良好,能够有效测定红参醇提过程中人参皂苷Rg1、Re和Rb1的量。
Objective To establish quantitative models to rapidly detect three ginsenoside monomer, which are ginsenoside Rg1, Re and Rb1, during extraction process of Radix Ginseng Rubra based on Near Infrared Spectroscopy（NIR）. Methods NIR spectra of extraction process were collected online and the contents of ginsenoside Rg1, Re and Rb1 were determined by ultra performance liquid chromatography（UPLC）. And then the quantitative calibration models were established between the spectra and the measured values by using Partial Least Squares（PLS） algorithm, to analyse extraction process online. Results The optimal wave number of ginsenoside Rg1 and Re were all in the range of 9 403.7-7 498.3 cm-1 and 6 102-5 446.3 cm-1, and the optimal wave number of Rb1 was in the range of 5 774.1-5 446.3 cm-1. The internal cross-certification decision coefficient（R2） of ginsenoside Rg1, Re and Rb1 were 99.40, 99.44, 99.41 respectively, and RMSECV were 5.18, 2.77, 11.00, respectively. Conclusion The results demonstrated that three quantitative models were satisfactory, which could effectively and rapidly detect the contents of ginsenoside Rg1, Re and Rb1 in the extraction process of Radix Ginseng Rubra.
作者
徐敏
张磊
岳洪水
鞠爱春
XU Min;ZHANG Lei;YUE Hongshui;JU Aichun(Department of Pharmaceutical Analysis, China Pharmaceutical University, Nanjing 210009, China;Tianjin Tasly Pride Pharmaceutical Co. Ltd., Tianjin 300402, China;Tianjin Key Laboratory of Safety Evaluation Enterprise of TCM Injections, Tianjin 300402, China)
出处
《药物评价研究》
CAS
2018年第3期462-468,共7页
Drug Evaluation Research
基金
天津市中药注射剂关键技术校企协同创新实验室建设项目(17PTSYJC00090)
天津市中药注射剂新技术企业重点实验室
