Application of Rotated PCA Models to Facilitate Interpretation of Metabolite Profiles: Commercial Preparations of St. John’s Wort

Abstract

This paper describes the application of orthogonal rotation of models based on principal component analysis (PCA) of (1)H nuclear magnetic resonance (NMR) spectra and high-performance liquid chromatography-photo diode array detection (HPLC-PDA) profiles of natural product mixtures using extracts of antidepressive pharmaceutical preparations of St. John's wort as an example. (1)H-NMR spectroscopy of complex mixtures is often used in metabolomic, metabonomic and metabolite profiling studies for assessment of sample composition. Interpretation of the derived chemometric models may be complicated because several sample properties often contribute to each principal component and because the influence of individual metabolites may be shared by several principal components. Furthermore, extensive signal overlap in (1)H-NMR spectra poses additional challenges to the interpretation of PCA models derived from such data. Orthogonal rotation of PCA models derived from (1)H-NMR spectra and HPLC-PDA profiles of the extracts of St. John's wort preparations facilitate interpretation of the model. Using the varimax criterion, rotation of loadings provides simpler conditions for understanding the influence of individual metabolites on the observed clustering. Alternatively, rotation of scores simplifies the understanding of the influence of whole metabolite profiles on the clustering of individual samples.

1H NMR-based metabolomics combined with HPLC-PDA-MS-SPE-NMR for investigation of standardized Ginkgo biloba preparations

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Metabolic profiling of Rhodiola rosea rhizomes by 1H NMR spectroscopy

“…This includes non‐selective response, high information content with respect to chemical structures present, speed, robustness, and the possibility of sample classification based on a large number of variables (Schripsema 2010; Kim et al ., 2010). Thus, 1 H NMR based metabolomic techniques are well‐suited for studies of variability of medicinal plants and derived products (Rasmussen et al ., 2006; Cardoso‐Taketa et al ., 2008; Kang et al ., 2008; Lawaetz et al ., 2009). However, such studies require carefully adjusted sample preparation procedures in order to accommodate a broad range of chemical structures and physicochemical properties represented in the extracts (Kim and Verpoorte, 2010).…”

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