茵栀黄口服液提取过程的近红外光谱在线监测模型和含量测定

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目的:采用近红外光谱技术对茵栀黄提取过程进行在线监测,实时反映有效成分绿原酸、黄芩苷和栀子苷的溶出信息,并建立其含量测定方法。方法:取不同时间点的茵栀黄提取液并采集其光谱信息,应用高效液相色谱法测定茵栀黄提取液中的绿原酸、黄芩苷和栀子苷的含量作为其化学真值。应用偏最小二乘法结合化学真值建立上述3种成分的定量分析模型。建模过程中,以决定系数R2、交叉验证误差均方根(RMSECV)和优化阶(Rank)为指标,确立用于建模的最优近红外波段和光谱预处理方法。结果:绿原酸、黄芩苷和栀子苷的最佳建模波段分别为9 401.6~7 498.2 cm-1、9 401.6~8 448.9 cm-1、9 401.6~7 498.2 cm-1,R2分别为0.9712、0.9826、0.9538,RMSECV分别为0.119、0.372、0.031,优化阶分别为6、3、6。将上述模型用于检测平行批次样品中绿原酸、黄芩苷和栀子苷的含量,得到其预测值的马氏距离(Mash dist)均小于1,其平均相对误差分别为:2.14%、0.47%和2.05%。结论:该实验模型的预测性能良好,建立的方法快速、便捷、准确,可对中药制剂生产过程控制提供科学依据。 OBJECTIVE: To monitor the extraction process of Yinzhihuang by near infrared spectroscopy and to reveal the dissolution of chlorogenic acid, baicalin and geniposide in real time and to establish a method for the determination of its content. Methods: Yinzhihuang extract at different time points was collected and its spectral information was collected. The contents of chlorogenic acid, baicalin and geniposide in Yinzhihuang extract were determined by HPLC as their chemical true value. The partial least squares method combined with chemical true value was used to establish the quantitative analysis model of the above three components. In the modeling process, the optimal near-infrared band and spectral preprocessing method for modeling are established with the determination coefficient R2, RMSECV and Rank as indexes. Results: The optimal modeling bands of chlorogenic acid, baicalin and geniposide were 9 401.6 ~ 7 498.2 cm-1,9 401.6 ~ 8 448.9 cm-1,9 401.6 ~ 7 498.2 cm-1, R2 were 0.9712,0.9826,0.9538, RMSECV respectively 0.119,0.372,0.031, the optimization order were 6,3,6. The above model was used to detect the contents of chlorogenic acid, baicalin and geniposide in parallel samples, and the predicted Mash dist values ​​were all less than 1. The average relative errors were 2.14% 0.47% and 2.05%. Conclusion: The experimental model has good predictive performance, and the established method is fast, convenient and accurate, which can provide a scientific basis for the control of the production process of traditional Chinese medicine preparation.
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