Metabolomics of four TCM herbal products: Application of HPTLC analysis

Abstract

This metabolomics study involves the multivariate analysis (MVA) of the HPTLC fingerprints of non-polar phyto-chemicals in four popular medicinal herbs' dried roots ‘<i>radix</i>’ (<i>Aster tataricus</i>, <i>Atractylodes lancea</i>, <i>Gentiana rigescens</i> and <i>Gentiana macrophylla</i>). These herbal products have been and are still being used in traditional Chinese medicine for treating many ailments. The extraction of these non-polar phyto-chemicals was carried out using petroleum ether and analysed by HPTLC using a developing solvent mixture of toluene–ethyl acetate (15 : 1). Three main MVAs were employed for statistical data exploration: Principal Component Analysis (PCA), Partial Least Squares-Discriminant Analysis (PLS-DA) and orthogonal PLS-DA. The model score plot results showed that all three MVAs showed very good spatial distributions with clear clusters/grouping of each herb. Also, statistically, all three models had high reproducibility and predictivity values (≫0.5). In conclusion, HPTLC with its simplicity and robustness should be explored in the application of MVA.