HPLC指纹图谱技术结合PLS-DA在辛芩颗粒质量控制中的应用

目的： 建立辛芩颗粒HPLC指纹图谱，结合偏最小二乘法-判别分析（PLS-DA），为其鉴别和质量控制提供参考。方法： 采用Waters Sunfire C₁₈（250 mm×4.6 mm，5μm）色谱柱；甲醇（A）-0.1%磷酸水溶液（B）为流动相，梯度洗脱；流速1.0 mL·min^-1；检测波长278 nm；柱温30℃。用中药色谱指纹图谱相似度评价系统（2012A版）建立指纹图谱共有模式和相似度计算，用SIMCA14.1软件建立PLS-DA模型做统计分析。结果： 建立了辛芩颗粒指纹图谱，确认28个共有峰，相似度为0.859~0.991，与对照品比对指认出10个成分，PLS-DA显示6个生产厂家17批辛芩颗粒的成分较为一致，但28个共有峰含量存在差异，并且确定了导致厂家间质量差异的关键成分。结论： 该研究建立指纹图谱方法有助于辛芩颗粒整体质量控制，同时为其质量评价提供一种有效手段。

Objective: To establish the HPLC fingerprint of Xinqin granules,and to provide reference for their identification and quality control through combining discriminant analysis of partial least squares (PLS-DA). Methods: The analysis was performed on a Waters Sunfire ODS C₁₈(250 mm×4.6 mm,5 μm))column with mobile phase consisted of methanol-0.1% phosphoric acid solution with gradient elution at a flow rate of 1.0 mL/min. The detection wavelength was set at 278 nm and the column temperature was 30℃. The fingerprint common pattern and similarity calculation were performed using traditional Chinese medicine chromatographic fingerprint similarity evaluation system(2012A).The PLS-DA model was established using SIMCA 14.1 software to perform statistical analysis. Results: The HPLC fingerprint of Xinqin Granules was established and 28 common peaks were identified in the study. The similarities were 0.859~0.991 and nine kinds of components were identified. PLS-DA analysis showed that components of the 17 batches of xinqin granules were consistent. However,there were differences in the contents of the 28 common components among different samples and key components that led to quality differences among manufacturers were identified. Conclusion: The developed mode identification method is helpful to overall quality control of Xinqin granules,which provides an effective approach to quality evaluation.

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