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 ‘radix’ (Aster tataricus, Atractylodes lancea, Gentiana rigescens and Gentiana macrophylla). 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.
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