Multi-component metabolic classification of commercial feverfew preparations via high-field 1H-NMR spectroscopy and chemometrics.

There is increasing interest in evaluating the clinical efficacy of herbal medicines. However, there are significant analytical problems associated with quality control and the measurement of the overall composition of such complex, multi-component mixtures as normally required in the pharmaceutical industry. Here we describe a novel NMR spectroscopic and pattern recognition analytical approach to investigate composition and variability of a commonly used herbal medicine. 600 MHz (1)H-NMR spectroscopy and principal components analysis (PCA) was used to discriminate between batches of 14 commercially available feverfew samples based on multi-component metabolite profiles. Two of the batches were significantly different from the other twelve. The twelve remaining classes could be classified into discrete groups by PCA on the basis of minor differences in overall chemical composition. NMR based pattern recognition (PR) analysis of extracts proved to be superior to PR analysis of HPLC traces of the same mixtures. This work indicates the potential value of NMR combined with PCA for the characterisation of complex natural product mixtures, and the discrimination of samples containing allegedly identical ingredients.