Comprehensive analysis of Polygoni Multiflori Radix of different geographical origins using ultra-high-performance liquid chromatography fingerprints and multivariate chemometric methods.
Comprehensive analysis of Polygoni Multiflori Radix of different geographical origins using ultra-high-performance liquid chromatography fingerprints and multivariate chemometric methods.
复制标题
超高效液相色谱指纹图谱和多元化学计量学方法综合分析不同产地何首乌
DOI：
10.1016/j.jfda.2016.11.009
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发表时间：
2018-01
影响因子：
3.6
通讯作者：
Ai-Di Qi
中科院分区：
文献类型：
--
作者：
Li-Li Sun;Meng Wang;Hui-Jie Zhang;Ya-Nan Liu;Xiao-Liang Ren;Yan-Ru Deng;Ai-Di Qi
何首乌（PMR）不仅是一种传统的草药，而且作为一种受欢迎的功能性食品也越来越多地被使用。本研究采用多元化学计量和质谱相结合的方法对6个不同产地的PMR进行了超高效液相色谱（UPLC）指纹图谱分析。提出了一种基于多元曲线解析-交替最小二乘（MCR-ALS）和三种分类方法的化学计量学策略，对获得的UPLC指纹图谱进行分析。常见的色谱问题，包括背景贡献、基线贡献和峰重叠，由建立的MCR-ALS模型处理。共解析了22个分量。此外，从MCR-ALS模型中获得相对物种浓度，并用于多变量分类分析。应用主成分分析（PCA）和Ward's方法对来自6个不同地理区域的72个PMR样本进行了分类。PCA得分图显示，PMR样本可分为4个聚类，这些聚类与源区的地理位置和气候有关。这些结果随后被沃德的方法所证实。此外，根据Ward方法获得的聚类中心之间的方差加权距离，确定了五个成分作为聚类判别最显著的变量（化学标记）。应用反传播人工神经网络对化学标记物在不同样品上的作用进行了确认和预测。最后，用uplc -四极杆飞行时间质谱仪对5种化学标记物进行鉴定。成分3、12、16、18和19分别鉴定为2,3,5,4 ' -四羟基二苯乙烯-2- o -β-d-葡萄糖苷，大黄素-8- o -β-d-葡萄糖苷，大黄素-8- o -（6 ' - o -乙酰基）-β-d-葡萄糖苷，大黄素和物理。综上所述，该方法可用于自然样品的综合分析。
Polygoni Multiflori Radix (PMR) is increasingly being used not just as a traditional herbal medicine but also as a popular functional food. In this study, multivariate chemometric methods and mass spectrometry were combined to analyze the ultra-high-performance liquid chromatograph (UPLC) fingerprints of PMR from six different geographical origins. A chemometric strategy based on multivariate curve resolution–alternating least squares (MCR–ALS) and three classification methods is proposed to analyze the UPLC fingerprints obtained. Common chromatographic problems, including the background contribution, baseline contribution, and peak overlap, were handled by the established MCR–ALS model. A total of 22 components were resolved. Moreover, relative species concentrations were obtained from the MCR–ALS model, which was used for multivariate classification analysis. Principal component analysis (PCA) and Ward's method have been applied to classify 72 PMR samples from six different geographical regions. The PCA score plot showed that the PMR samples fell into four clusters, which related to the geographical location and climate of the source areas. The results were then corroborated by Ward's method. In addition, according to the variance-weighted distance between cluster centers obtained from Ward's method, five components were identified as the most significant variables (chemical markers) for cluster discrimination. A counter-propagation artificial neural network has been applied to confirm and predict the effects of chemical markers on different samples. Finally, the five chemical markers were identified by UPLC–quadrupole time-of-flight mass spectrometer. Components 3, 12, 16, 18, and 19 were identified as 2,3,5,4′-tetrahydroxy-stilbene-2-O-β-d-glucoside, emodin-8-O-β-d-glucopyranoside, emodin-8-O-(6′-O-acetyl)-β-d-glucopyranoside, emodin, and physcion, respectively. In conclusion, the proposed method can be applied for the comprehensive analysis of natural samples.