目的 建立甘草Glycyrrhiza uralensis超高效液相色譜（UPLC）指紋圖譜及多成分定量分析方法，并結(jié)合化學(xué)計(jì)量學(xué)進(jìn)行不同產(chǎn)地甘草的質(zhì)量分析和評(píng)價(jià)。方法 采用UPLC法建立30批不同產(chǎn)地甘草的指紋圖譜，使用OriginPro 2024及SIMCA14.1軟件進(jìn)行Pearson相關(guān)性分析、聚類分析（cluster analysis，CA）、主成分分析（principal component analysis，PCA）、正交偏最小二乘判別分析（orthogonal partial least squares discriminant analysis，OPLS-DA）和接受者操作特征曲線分析，并對(duì)甘草中6種成分進(jìn)行定量測(cè)定。結(jié)果 建立的UPLC指紋圖譜共標(biāo)定了23個(gè)共有峰，Pearson相關(guān)性分析表明各共有峰之間具有復(fù)雜的相關(guān)性，CA和PCA均表明，8個(gè)產(chǎn)地樣本分布較為獨(dú)立集中，部分甘肅樣本與其它省份樣本間有一定相似性，但樣本整體被分為甘肅、內(nèi)蒙古、新疆3大類，23個(gè)共有峰被分為5組；OPLS-DA則可實(shí)現(xiàn)對(duì)3省產(chǎn)地樣本的有效區(qū)分，并篩選了10個(gè)差異性標(biāo)志物；3省甘草樣本中6個(gè)定量成分間的差異情況與OPLS-DA分析結(jié)果相近。結(jié)論 建立的甘草UPLC指紋圖譜及多指標(biāo)定量分析方法穩(wěn)定、可靠，結(jié)合化學(xué)計(jì)量學(xué)分析可用于為甘草藥材的產(chǎn)地質(zhì)量評(píng)價(jià)和產(chǎn)地選擇提供參考。

Objective To establish the ultra-high performance liquid chromatography (UPLC) fingerprint and multi-component quantitative analysis method of Glycyrrhiza uralensis, and to combine with chemometrics for the quality analysis and evaluation of G. uralensis from different origins. Methods The fingerprints of 30 batches of G. uralensis from different origins were established by UPLC. Pearson correlation analysis, cluster analysis (CA), principal component analysis (PCA), orthogonal partial least squares discriminant analysis (OPLS-DA), and receiver operating characteristic curve analysis were performed using OriginPro 2024 and SIMCA14.1 software. A total of six components in G. uralensis were quantitatively determined. Results A total of 23 common peaks were calibrated in the established UPLC fingerprint. Pearson correlation analysis revealed a complex correlation between the common peaks. The results of the CA and PCA analyses indicated that the distribution of samples from the eight origins (cities) was relatively independent and concentrated. Some samples from Gansu province exhibited similarities with samples from other provinces, however, all samples could be classified into three categories (provinces): Gansu, Inner Mongolia, and Xinjiang. The 23 common peaks could be divided into five groups. OPLS-DA can effectively distinguish between the samples from three provinces, and 10 differential markers were screened. The differences between the six quantitative components in G. uralensis samples from three provinces were similar to the results of the OPLS-DA analysis. Conclusion The established UPLC fingerprint and multi-index quantitative analysis method of G. uralensis are stable and reliable. Combined with chemometric analysis, it can be used to provide a reference for the quality evaluation and selection of origin of G. uralensis.

R286.2

湖北省科技重大專項(xiàng)（2020ACA007）；湖北省科技重大專項(xiàng)（2022ACA003）；黃石市揭榜制科技項(xiàng)目（2023A005）