基于近红外光谱技术的连翘药材质量控制方法研究

目的： 采用近红外光谱技术，建立痰热清注射液原料药材之一连翘的质量控制方法。 方法： 利用主成分判别分析（PCA-DA）方法对连翘药材的产地和采收时期进行了定性鉴别；利用PLS方法建立定量校正模型，对连翘原料药材中青翘所占比例进行定量分析；在此基础上提出了基于近红外光谱指纹图谱相似度匹配值的连翘药材质控方法。 结果： 所建产地和采收时期判别分析模型的错判率分别为3.37%和1.12%；对所建青翘所占比例定量预测模型进行外部验证，相关系数和预测偏差分别为0.9500和5.07%；利用3σ原理确定了所建相似度匹配模型的阈值为96.19，所建模型可反映厂方所用原料药材与市售药材的差异。 结论： 本文所建的方法可为连翘药材的采购和投料前检验提供技术手段，对于解决中成药原料药材的质量控制问题具有一定的借鉴意义。

Objective: To develop an NIR spectroscopy-based method for the quality control of Forsythia Suspense,which is a raw material of Tanreqing injection. Method: Principal component analysis-discriminant analysis(PCA-DA)was applied to classify the geographical origins of the samples,and partial least-square regression(PLSR)method was used to establish the calibration model for the quantitative prediction of the ratio of green Forsythia Suspense in the raw materials.Based on the research,a method of NIR spectroscopy fingerprint was proposed for the quality control of Forsythia Suspense. Results: The error probability of the PCA-DA models for the geographical origins and harvest time was 3.37% and 1.12%,respectively.The performance of the calibration model for the quantitative prediction of the ratio of green Forsythia Suspense in the raw materials was evaluated,and the root mean square error of prediction(RMSEP)and correlation coefficient(R)with the results were 5.07% and 0.9500,respectively.The threshold for the similarity match model was set at 96.19 according to the 3σ principles,and the differences between the raw materials and the samples collected from the medical materials market could be reflected using the NIR spectroscopy-based fingerprints. Conclusions: The proposed methods can be used for the quality control of Forsythia Suspense,which also provide promising tools for the quality control of other Chinese medicinal materials.

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