Simultaneous determination and difference evaluation of 14 ginsenosides in Panax ginseng roots cultivated in different areas and ages by high-performance liquid chromatography coupled with triple quadrupole mass spectrometer in the multiple reaction–monitoring mode combined with multivariate statistical analysis

Abstract

BackgroundGinsenosides are not only the principal bioactive components but also the important indexes to the quality assessment of Panax ginseng Meyer. Their contents in cultivated ginseng vary with the growth environment and age. The present study aimed at evaluating the significant difference between 36 cultivated ginseng of different cultivation areas and ages based on the simultaneously determined contents of 14 ginsenosides.MethodsA high-performance liquid chromatography (HPLC) coupled with triple quadrupole mass spectrometer (MS) method was developed and used in the multiple reaction–monitoring (MRM) mode (HPLC-MRM/MS) for the quantitative analysis of ginsenosides. Multivariate statistical analysis, such as principal component analysis and partial least squares-discriminant analysis, was applied to discriminate ginseng samples of various cultivation areas and ages and to discover the differentially accumulated ginsenoside markers.ResultsThe developed HPLC-MRM/MS method was validated to be precise, accurate, stable, sensitive, and repeatable for the simultaneous determination of 14 ginsenosides. It was found that the 3- and 5-yr-old ginseng samples were differentiated distinctly by all means of multivariate statistical analysis, whereas the 4-yr-old samples exhibited similarity to either 3- or 5-yr-old samples in the contents of ginsenosides. Among the 14 detected ginsenosides, Rg1, Rb1, Rb2, Rc, 20(S)-Rf, 20(S)-Rh1, and Rb3 were identified as potential markers for the differentiation of cultivation ages. In addition, the 5-yr-old samples were able to be classified in cultivation area based on the contents of ginsenosides, whereas the 3- and 4-yr-old samples showed little differences in cultivation area.ConclusionThis study demonstrated that the HPLC-MRM/MS method combined with multivariate statistical analysis provides deep insight into the accumulation characteristics of ginsenosides and could be used to differentiate ginseng that are cultivated in different areas and ages.

Stem-leaves of Panax as a rich and sustainable source of less-polar ginsenosides: comparison of ginsenosides from Panax ginseng, American ginseng and Panax notoginseng prepared by heating and acid treatment

“…PG contained three major types of polar ginsenosides, namely, Rb1 (0.53 ± 0.01 mg/g), Re/Rg1 (2.21 ± 0.18 mg/g) and Rb2 (0.36 ± 0.01 mg/g), and these species accounted for the majority of the total ginsenoside content. The kinds of ginsenosides identified were consistent with previous reports [ 25 , 26 ]; however, the contents were lower than those reported for white ginseng roots. These ginseng roots were harvested earlier in the season, which is likely responsible for this deviation [ 27 , 28 ].…”

The Difference between White and Red Ginseng: Variations in Ginsenosides and Immunomodulation

“…2). In P. ginseng C. A. Meyer, only dammarane-type (both PPD and PPT type) and oleanane-type ginsenosides are detectable, while the ocotillol type exist mainly in the P. quinquefolius L. To date, more than 50 different ginsenosides have been identified in ginseng [16][17][18][19]. The content of ginsenosides in ginseng varies according to the growth/harvest location, harvest season, age, and part of the plant [18][19][20][21][22][23][24][25][26][27][28].…”

Machine learning methods to predict the cultivation age of Panacis Quinquefolii Radix

“…Similar models for rhizome age prediction were developed using IR spectroscopy combined with PLS regression, and the RMSEP value can be as low as 0.036 [ 15 ]. Other statistical analysis methods including principal component analysis (PCA), hierarchical cluster analysis, and linear discriminant analysis could also discriminate the growth years of ginseng roots by their chemical profiles measured by other analytical methods [ 16 – 18 ]. However, most of the statistical analyses implemented in the above studies are linear in nature, which are unable to fully capture the intrinsic relationships between physicochemical profiles and growth year, and would easily fail for complex prediction tasks (we will compare the performance of linear and non-linear models later).…”