目的 采用指纹图谱、多指标成分定量与化学计量法相结合的方法，评价不同产地白术Atractylodes macrocephala的质量，为白术的质量评价与质量控制体系的构建及其差异标志物的探索提供参考和依据。方法 采用UPLC法，建立22批不同产地白术指纹图谱，测定白术中的9个指标性成分含量；使用Hiplot科研绘图平台对白术进行聚类热图分析，使用SPSS 26.0软件对白术进行主成分分析（principal component analysis，PCA），使用SIMCA 14.0软件对白术进行正交偏最小二乘法-判别分析（orthogonal partial least squares-discriminant analysis，OPLS-DA）。结果 建立了22批白术药材的指纹图谱，相似度为0.781～0.997；聚类热图分析结果显示，22批白术明显分为2类；PCA得到2个主成分的累积方差贡献率为76.536%，说明这2个成分在反映不同产地白术样品共有成分关系中起到主导作用。OPLS-DA结果表明，新绿原酸、隐绿原酸、绿原酸、阿魏酸、异绿原酸C这5个成分可能是影响白术药材的差异标志物。结论 该方法专属性强、结果准确，结合化学模式识别可有效区分不同产地白术，为其质量标准完善及差异标志物发掘提供依据。

Objective To evaluate the quality analysis of Baizhu (Atractylodis Macrocephalae Rhizoma, AMR) from different origins by combining fingerprinting, quantification of multiple indicator components and chemometrics, and to provide reference and basis for the construction of the quality evaluation and quality control system of AMR as well as the exploration of its potential quality markers. Methods The fingerprints of 22 batches of AMR from different origins were established by ultra performance liquid chromatography (UPLC) to determine the contents of nine index components in AMR, the clustering heat map analysis was performed on AMR by using Hiplot scientific research mapping platform, the principal component analysis (PCA) of AMR by using SPSS 26.0 software, and the orthogonal partial least squares-discriminant analysis (OPLS-DA) of AMR by using SIMCA 14.0 software. Discriminant analysis (OPLS-DA) using SIMCA 14.0 software. Result The fingerprints of 22 batches of AMR were established, and the similarities ranged from 0.781 to 0.997. The results of cluster heat map analysis showed that the 22 batches of AMR were divided into two groups; the cumulative variance contribution ratio of the two principal components obtained from the PCA was 76.536%, which indicated that the two components played a dominant role in reflecting the relationship between the common components of AMR samples from different origins. Neochlorogenic acid, cryptochlorogenic acid, chlorogenic acid, ferulic acid, and 4,5-dicaffeoylquinic acid are five components that may affect the potential quality markers of AMR herb quality. Conclusion This method is exclusive and accurate, and when combined with chemical pattern recognition, it can effectively differentiate AMR from different origins, providing a basis for improving its quality standard and discovering quality markers.

R286.12

中国医学科学院医学与健康科技创新工程(2021-I2M-1-071)