目的 建立黄芪Astragali Radix标准汤剂的物理指纹图谱及粒径指纹图谱，用于区分仿野生黄芪与平栽黄芪2种品规药材的质量，进一步发现中药质量标志物（quality marker，Q-Marker）的物理化学指标。方法 制备10批仿野生与12批平栽黄芪标准汤剂，以电导率、溶解性固体总量、盐度、电阻率、pH值、ζ电位、多分散指数（polydispersity index，PDI）、Z均值（平均粒径）8个物理化学指标构建黄芪标准汤剂物理指纹图谱；采用流式细胞仪测定黄芪标准汤剂100～200、200～300、300～400、400～500、500～600、600～700、700～800、800～900、900～1 000 nm 9个粒径区间颗粒数量占比，构建粒径指纹图谱；应用相似度评价、主成分分析（principal component analysis，PCA）、层次聚类分析（hierarchical cluster analysis，HCA）对物理和粒径指纹图谱进行分析评价。结果 建立了22批仿野生与平栽黄芪标准汤剂的物理指纹图谱，多元统计分析结合Mann-Whitney U检验分析发现，ζ电位与Z均值是区分2种品规黄芪的敏感物理化学指标，且仿野生黄芪的ζ电位绝对值及Z均值均显著低于平栽黄芪（P＜0.05）；进一步检测黄芪标准汤剂粒径区间颗粒数量占比，发现100～200 nm占比最大，200～300 nm次之，其余区间颗粒数量占比较小。粒径指纹图谱PCA和HCA结果表明，仿野生黄芪和平栽黄芪有明显分离的趋势。差异分析表明，仿野生黄芪在100～200 nm粒径区间颗粒数量占比显著高于平栽黄芪，剩余8个粒径区间颗粒数量占比，平栽黄芪均显著高于仿野生黄芪（P＜0.05）；综合偏最小二乘-判别分析（partial least squares-discriminant analysis，PLS-DA）和随机森林筛选潜在特征粒径区间，发现400～500、500～600 nm粒径区间对于区分2种品规黄芪的贡献最大。结论 从中药多分散体系角度，用多指标物理指纹图谱与粒径指纹图谱获得了3个参数：ζ电位、Z均值与粒径区间颗粒数量占比，可对2种品规的黄芪进行质量差异性评价，初步筛选了2个潜在特征粒径区间用以区分不同品规黄芪，为黄芪的质量控制和Q-Marker新指标的发现提供研究依据。

Objective To establish a physical fingerprint and a particle size fingerprint of standard decoction of Huangqi (Astragali Radix, AR) for distinguishing the quality of two types of crude drugs: wild-simulated and horizontally transplanted AR, and further discover the physicochemical indicators of traditional Chinese medicine quality markers. Methods Ten batches of wild-simulated and twelve batches of transplanted AR standard decoctions were prepared. A physical fingerprint was constructed using eight physicochemical indices, including conductivity, total dissolved solids, salinity, resistivity, pH value, ζ potential, polydispersity index (PDI), and Z-average (average particle size). A particle size fingerprint was established by determining the particle number percentages within nine particle size intervals (100—200, 200—300, 300—400, 400—500, 500—600, 600—700, 700—800, 800—900, and 900—1 000 nm) using a flow cytometer. Similarity evaluation, principal component analysis (PCA), and hierarchical cluster analysis (HCA) were applied to analyze the physical fingerprints and particle size fingerprints. Results Physical fingerprints of 22 batches of wild-simulated AR and transplanted AR standard decoctions were established. Multivariate statistical analysis combined with the Mann-Whitney U test revealed that ζ potential and Z-average were sensitive physicochemical indicators for distinguishing the two specifications of AR. Moreover, the absolute value of ζ potential and the Z-average of wild-simulated AR were significantly lower than those of transplanted AR (P < 0.05). Further analysis of the particle count percentage in standard decoction of AR showed that particles in the 100–200 nm interval accounted for the highest percentage, followed by the 200—300 nm range, while the remaining size intervals showed smaller percentages. PCA and HCA of the particle size fingerprints showed a clear separation trend between wild-simulated and transplanted AR. Differential analysis indicated that the particle count percentage of wild-simulated AR was significantly higher than that of transplanted AR in the 100—200 nm interval, whereas transplanted AR had significantly higher percentages in the other eight intervals (P < 0.05). Subsequently, by integrating the potential characteristic particle size intervals identified by partial least squares-discriminant analysis (PLS-DA) and random forest, the 400—500 nm and 500—600 nm intervals were found to contribute the most to distinguishing the two specifications of AR. Conclusion The quality differences between two specifications of AR were evaluated from the perspective of the polydisperse system, using multi-index physical fingerprints and particle size fingerprints. Three parameters were identified for quality evaluation, including ζ potential, Z-average, and the particle count percentages in particle size intervals. Two potential characteristic particle size intervals were preliminarily screened to distinguish different specifications of AR, providing a research basis for its quality control and discovery of new indicators for quality markers.

R283.6

国家自然科学基金联合基金项目（U23A20517）