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- W4280563246 abstract "Abstract Background It is well known that ginsenosides are the main active ingredients in ginseng, and they have also been important indexes for assessing the quality of ginseng. However, the absolute contents of ginsenosides in ginseng were shown to be varied with the origin, cultivated type, cultivated year and climate. It is a great challenge to distinguish the commercial types of ginsengs based on the absolute content of ginsenosides. Methods The common commercial types of ginsengs in China are white ginseng (WG), red ginseng (RG), American ginseng (AG). To clearly illustrate the differences among WG, RG and AG at the ginsenosides level, we established a strategy for the detection and identification of ginsenosides based on an optimized LC-Q-Orbitrap MS/MS method coupled with an in-house database of ginsenosides. A special normalization to the datasheet was also introduced prior to multivariate statistical analysis and logistic regression analysis. Results Here, 81 ginsenosides were identified in different ginseng samples. The majority of the ginsenosides (59 in 81) were all shared by WG, RG and AG. Our analysis strategy clearly divided the ginseng samples into three groups (i.e., WG, RG and AG groups). The ginsenoside profiles in RG and WG were significantly different from those in AG. The potential markers and multivariate diagnostic models differentiating the three types of ginsengs were indicated by the principal component analysis (PCA) and logistic regression analysis. Conclusion Our novel methodology according to ginsenoside profiles is more robust than that based on single ingredient, and could be widely used to distinguish the commercial types of ginsengs." @default.
- W4280563246 created "2022-05-22" @default.
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- W4280563246 date "2022-05-16" @default.
- W4280563246 modified "2023-09-24" @default.
- W4280563246 title "A novel strategy to interpret the differences among white ginseng, red ginseng and American ginseng based on normalized ginsenoside profiles" @default.
- W4280563246 doi "https://doi.org/10.21203/rs.3.rs-1623990/v1" @default.
- W4280563246 hasPublicationYear "2022" @default.
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