Evaluation of four different analytical tools to determine the regional origin of Gastrodia elata and Rehmannia glutinosa on the basis of metabolomics study

Abstract

Chemical profiles of medicinal plants could be dissimilar depending on the cultivation environments, which may influence their therapeutic efficacy. Accordingly, the regional origin of the medicinal plants should be authenticated for correct evaluation of their medicinal and market values. Metabolomics has been found very useful for discriminating the origin of many plants. Choosing the adequate analytical tool can be an essential procedure because different chemical profiles with different detection ranges will be produced according to the choice. In this study, four analytical tools, Fourier transform near‑infrared spectroscopy (FT-NIR), 1H-nuclear magnetic resonance spectroscopy (1H‑NMR), liquid chromatography-mass spectrometry (LC-MS), and gas chromatography-mass spectroscopy (GC-MS) were applied in parallel to the same samples of two popular medicinal plants (Gastrodia elata and Rehmannia glutinosa) cultivated either in Korea or China. The classification abilities of four discriminant models for each plant were evaluated based on the misclassification rate and Q2 obtained from principal component analysis (PCA) and orthogonal projection to latent structures-discriminant analysis (OPLS‑DA), respectively. 1H-NMR and LC-MS, which were the best techniques for G. elata and R. glutinosa, respectively, were generally preferable for origin discrimination over the others. Reasoned by integrating all the results, 1H-NMR is the most prominent technique for discriminating the origins of two plants. Nonetheless, this study suggests that preliminary screening is essential to determine the most suitable analytical tool and statistical method, which will ensure the dependability of metabolomics-based discrimination.

Figure 1

PCA score plots of G. elata (a, FT-NIR; b, ¹H-NMR; c, GC-MS; d, LC-MS) and R. glutinosa (e, FT-NIR; f, ¹H-NMR; g, GC-MS; h, LC-MS). Red triangles for Chinese samples; black diamonds for Korean samples.

Figure 2

PCA column plots and misclassification rates of G. elata (a, FT-NIR; b, ¹H-NMR; c, GC-MS; d, LC-MS) and R. glutinosa (e, FT-NIR; f, ¹H-NMR; g, GC-MS; h, LC-MS). Red for Chinese samples; black for Korean samples.

Figure 3

PC1 column plots of R. glutinosa analyzed by GC-MS (enlarged version of Figure 2g). Ambiguous samples and misclassified samples are indicated.

Figure 4

OPLS-DA score plots of G. elata (FT-NIR; a, ¹H-NMR; b, GC-MS; c, LC-MS; d) and R. glutinosa (FT-NIR; e, ¹H-NMR; f, GC-MS; g, LC-MS; h). Red triangles for Chinese samples; black diamonds for Korean samples.