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Original Paper
Analytical Methods | Planta Med 2006; 72: 556-563
DOI: 10.1055/s-2006-931567 |
© Georg Thieme Verlag KG Stuttgart · New York |
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Multivariate Analysis of Integrated and Full-Resolution 1H-NMR Spectral Data from Complex Pharmaceutical Preparations: St. John's Wort |
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| Bonnie Rasmussen1, Olivier Cloarec2, Huiru Tang2,3, Dan Stærk1, Jerzy W. Jaroszewski1 |
1 Department of Medicinal Chemistry, The Danish University of Pharmaceutical Sciences, Copenhagen, Denmark
2 Section of Biological Chemistry, Division of Biomedical Sciences, Faculty of Medicine, Imperial College London, London, U.K.
3 State Key Laboratory of Magnetic Resonance and Molecular and Atomic Physics, Wuhan Institute of Physics and Mathematics, The Chinese Academy of Sciences, Wuhan, P. R. China |
Abstract
Commercial herbal preparations are typically very complex mixtures and the relationship between content of various constituents and pharmacological action of the formulation is usually unclear. Such formulations are nevertheless standardized using a single marker constituent or a group of closely related constituents, which provides no information about other abundant constituents present in the extract. In this study, principal component analysis of 600 MHz 1H-NMR spectra of extracts of commercial formulations of St. John's wort (Hypericum perforatum), acquired in methanol-d4 and DMSO-d6, was shown to be able to discriminate between various preparations according to their global composition, including differentiation between various batches from the same supplier, while no clustering into classes of tablets and capsules was observed. This suggests that the plant extract variability rather than the manufacturing process accounts for the data clustering. Major variations in the content of flavonoids, recently linked to the antidepressant activity of St. John's wort extracts, were detected. Use of two NMR solvents provided complementary data sets, allowing assessment of various aspects of sample composition from separate PCA models. Both integrated (about 200 variables) and full-resolution NMR data (about 30 000 variables) have been used. The latter approach, applied for the first time in analysis of a herbal preparation, provided via loading plots more precise information about constituents responsible for data clustering, and may be generally preferable for PCA analysis of NMR data of plant extracts and herbal medicines.
Key words
St. John's wort - NMR spectroscopy - pattern recognition - principal component analysis - data reduction - quality control
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