基于UPLC-QTRAP-MS/MS和多元统计分析评价不同干燥方式对乌药叶成分的影响

Evaluation of the effects of different drying methods on the composition of Lindera aggregata leaf based on UPLC-QTRAP-MS/MS and multivariate statistical analysis

关键词:  乌药叶  超高效液相色谱-串联四级杆/线性离子阱质谱  方差分析  聚类热图  熵权TOPSIS  灰色关联

Key words:  Lindera aggregata leaf  QTRAP UPLC-MS/MS  ANOVA  clustering heat map  TOPSIS  GRA

摘要:

目的 基于UPLC-QTRAP-MS/MS和多元统计分析，考察和评价不同干燥方式对乌药叶成分的影响。方法 采用超高效液相色谱-串联四级杆/线性离子阱质谱(UPLC-QTRAP-MS/MS)技术，以ACQUITY UPLCHSS T3（100 mm×2.1 mm，1.8 μm）色谱柱分离，水（含0.1%甲酸A相）-乙腈（B相）为流动相；梯度洗脱，柱温35 ℃，流量0.2 mL·min-1，多反应离子监测（MRM）模式同时测定乌药叶10个成分，结合方差分析、聚类热图、熵权TOPSIS和灰色关联法对不同干燥方式后乌药叶多元化学成分进行综合评价。结果 10个成分在一定的浓度范围内线性良好，相关系数均大于0.9924；精密度、重复性和稳定性良好，RSD均小于5%；加样回收率在99.13%~103.07%，RSD值均小于5%。方差分析表明不同干燥方式后乌药叶6个成分具有显著性差异（p＜0.05）；聚类热图结果显示经不同干燥方式后各成分呈明显聚集趋势；TOPSIS与灰色关联法分析结果基本一致，最终经阴凉通风摊晾干燥处理后成分保留效果最佳。结论 所建立的方法准确、可靠，可为乌药叶适宜的加工方式和品质规范化生产提供参考。

Abstract:

OBJECTIVE To investigate and evaluate the effects of different drying methods on the composition of Lindera aggregata leaf based on ultra high performance liquid chromatography tandem fourth stage rod/linear ion trap mass spectrometry (UPLC-QTRAP-MS/MS) and multivariate statistical analysis. METHODS UPLC-QTRAP-MS/MS technology was used for simultaneously determine 10 components of Lindera aggregata leaf, with ACQUITY UPLCHSS T3 (100 mm×2.1 mm, 1.8 μm) as chromatographic column. The mobile phase consisted of water (containing 0.1% formic acid phase A)- acetonitrile (phase B), and the elution method was gradient elution. The column temperature was 35 ℃, the flow rate was 0.2 mL?min-1, and the detection mode was multi-reactive ion monitoring (MRM). Combined with analysis of variance, cluster heat map, entropy weighted TOPSIS, and grey correlation method, the multiple chemical components of Lindera aggregata leaf after different drying methods were evaluated comprehensively. RESULTS The 10 chemical components showed good linearity within a certain concentration range, and the correlation coefficients were all greater than 0.9924. The precision, repeatability, and stability were good, with RSD less than 5%. The average recoveries ranged from 99.13% to 103.07%, and the RSD values were all less than 5%. The results of variance analysis indicated that there were significant differences in the 6 components of Lindera aggregata leaf after different drying methods (p<0.05). Meanwhile, the clustering heatmap results demonstrated that these components displayed a clear clustering trend. Moreover, the results of TOPSIS and grey correlation method were basically consistent. Ultimately, the best components retention effect was achieved through cool and ventilated air spreading. CONCLUSION The established method is accurate and reliable, and can provide reference for suitable processing methods and standardized quality production of Lindera aggregata leaf.