不同产地大枣的UPLC-MS/MS多指标含量测定及化学模式识别研究

UPLC-MS/MS Based Quantification of Multi-index Components and Chemical Pattern Recognition Study for Jujubae Fructus from Different Producing Areas

关键词:  大枣  多指标含量测定  化学模式识别  质量控制

Key words:  Jujubae Fructus  multi-index content determination  chemical pattern recognition  quality control

摘要:

目的 建立不同产地大枣多指标含量测定的UPLC-MS/MS方法，为大枣的质量控制提供参考。方法 采用UPLC-MS/MS多反应监测负离子扫描模式，Shim-pack GIST C₁₈（2.1 mm×100 mm，2 μm）色谱柱进行分离，流动相为0.1%甲酸水溶液（A）-乙腈（B），进行梯度洗脱，测定25批大枣中环磷腺苷、绿原酸、咖啡酸、芦丁、白桦脂酸、路路通酸和齐墩果酮酸的含量，并结合主成分分析（principal component analysis，PCA）、层次聚类分析（hierarchical cluster analysis，HCA）和偏最小二乘判别分析（partial least squares discrimination analysis，PLS-DA）评价不同产地大枣中7种成分的差异。结果 7种成分在相应的浓度范围内线性关系良好，相关系数（r） ≥ 0.995 6，仪器精密度RSD<3.55%，平均加样回收率为99.17%~100.74%，RSD<3.48%。PCA和HCA结果表明，不同产地的大枣可明显分开，组内样品成分具有很强的相似性，而组间差异较大。通过PLS-DA中变量重要性投影分析发现4个差异性指标成分，分别为白桦脂酸、齐墩果酮酸、路路通酸及芦丁。结论 建立了专属性强、灵敏度高的大枣多指标含量测定方法，且通过统计方法证明白桦脂酸、齐墩果酮酸、路路通酸及芦丁为大枣质量差异性评价的指标成分，为不同产地大枣的区分及质量控制提供参考。

Abstract:

OBJECTIVE To establish UPLC-MS/MS method for the determination of multiple indexes of Jujubae Fructus from different producing areas, and to provide reference for the quality control of Jujubae Fructus. METHODS The separation was performed on a Shim-pack GIST C₁₈(2.1 mm×100 mm, 2 μm) column using UPLC-MS/MS multiple reaction monitoring in negative ion scanning mode. Gradient elution was performed with a mobile phase consisting of 0.1% formic acid in water(A) and acetonitrile(B) to determine the contents of adenosine cyclophosphate, chlorogenic acid, caffeic acid, rutin, betulinic acid, betulonic acid and oleanolic acid in 25 batches of Jujubae Fructus. In addition, chemometrics methods including principal component analysis(PCA), hierarchical cluster analysis(HCA) and partial least squares discriminant analysis(PLS-DA) were used to evaluate the differences of 7 components between Jujubae Fructus from different producing areas. RESULTS The linear relationship of the 7 components was good in the corresponding concentration range, the correlation coefficient(r) was ≥ 0.995 6, the RSD of instrument precision was <3.55%, the average recoveries were between 99.17% and 100.74%, and RSD was <3.48%. The results of PCA and HCA showed that the Jujubae Fructus from different producing areas could be separated obviously, and the composition of the samples within the groups had strong similarity, but the differences among the groups were great. According to the variable importance in project analysis of PLS-DA, 4 different index components were found, namely betulinic acid, oleanolic acid, betulonic acid and rutin. CONCLUSION A method with strong specificity and high sensitivity is established for the determination of multiple indexes of Jujubae Fructus, and the statistical method proves that betulinic acid, oleanolic acid, betulonic acid and rutin are the index components for the quality evaluation of Jujubae Fructus from different producing areas, which can providing reference for the distinction and quality control of Jujubae Fructus from different producing areas.