Combining PARAFAC analysis of HPLC-PDA profiles and structural characterization using HPLC-PDA-SPE-NMR-MS experiments:: Commercial preparations of St. John's wort
Combining PARAFAC analysis of HPLC-PDA profiles and structural characterization using HPLC-PDA-SPE-NMR-MS experiments:: Commercial preparations of St. John's wort
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DOI：
10.1021/ac702064p
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发表时间：
2008-03-15
影响因子：
7.4
通讯作者：
Staerk, Dan
中科院分区：
文献类型：
--
作者：
Schmidt, Bonnie;Jaroszewski, Jerzy W.;Staerk, Dan
草药制剂是非常复杂的混合物，可能含有多种药理活性物质。因此，对这类混合物的组成进行全球表征的方法是有意义的。在这项工作中，描述了来自几个大洲的贯叶连翘(金丝桃)商品制剂提取物的化学计量学分析。在洗脱模式下，使用相关优化翘曲来对齐光谱高效液相色谱图谱，以消除由于基质效应引起的保留时间漂移引起的峰不对齐。此外，HPLC-PDA-SPE-NMR-MS(SPE=固相萃取)实验也有助于翘曲，该实验产生了H-1核磁共振和C-13核磁共振数据(来自H-1检测到的异核关联)，以及ESI-MS和HRMS数据，从而能够鉴定所有主要混合物成分。采用并行因子分析(PARAFAC)，这是一种化学计量学方法，是主成分分析(PCA)在多向数据阵列中的推广。从PARAFAC分析中获得的峰面积的主成分分析被用来促进样品比较，并允许直接解释导致各制剂之间组成差异的成分。此外，PARAFAC分析的进样量提供了纯洗脱曲线和纯UV光谱，即使是洗脱峰也是如此，从而能够鉴定色谱未分离的组分。总之，PARAFAC对容易获得的高效液相-PDA数据的分析提供了对草药制剂的成分进行无监督和公正的评估的手段，这对评估其药理活性和临床疗效具有重要意义。
Herbal preparations represent very complex mixtures, potentially containing multiple pharmacologically active entities. Methods for global characterization of the composition of such mixtures are therefore of pertinent interest. In this work, chemometric analysis of high-performance liquid chromatography with photodiode-array detection (HPLC-PDA) data from extracts of commercial preparations of Hypericum perforatum (St. John's wort) that originate from several continents is described. The spectral HPLC profiles were aligned in the elution mode using correlation optimized warping in order to remove peak misalignment caused by retention time shifts due to matrix effects. Furthermore, the warping was assisted by HPLC-PDA-SPE-NMR-MS (SPE = solid-phase extraction) experiments that yielded H-1 NMR and C-13 NMR data (from H-1-detected heteronuclear correlations), as, well as ESI-MS and HRMS data, which enabled the identification of all major mixture constituents. The pre-processed HPLC-PDA data were subjected to parallel factor analysis (PARAFAC), a chemometric method that is a generalization of principal component analysis (PCA) to multi-way data arrays. PCA of the peak areas obtained from the PARAFAC analysis was used to facilitate sample comparison and allowed straightforward interpretation of constituents responsible for the differences in composition between individual preparations. In addition, loadings from the PARAFAC analysis provided pure elution profiles and pure UV spectra even for coeluting peaks, thus enabling the identification of chromatographically unresolved components. In conclusion, PARAFAC analysis of the readily accessible HPLC-PDA data provides the means for unsupervised and unbiased assessment of the composition of herbal preparations, of interest for assessment of their pharmacological activity and clinical efficacy.