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  • Title: Discrimination of three varieties of Codonopsis Radix based on fingerprint profiles of oligosaccharides by high performance liquid chromatography- evaporative light scattering detector combined with multivariate analysis.
    Author: Bai R, Wang Y, Cao Y, Yu H, Li P, Chai G, Li X, Li W, Cui F, Hu F.
    Journal: J Chromatogr A; 2022 Dec 06; 1685():463642. PubMed ID: 36395248.
    Abstract:
    Codonopsis Radix (CR) is a plant that is important in the practice of traditional Chinese medicine (TCM). The Chinese Pharmacopoeia 2020 records dried roots prepared from three varieties of Campanulaceae plants under the designation CR ("Dang-shen" in Chinese), including Codonopsis pilosula (Franch.) Nannf (C. pilosula), Codonopsis pilosula Nannf.var.modesta (Nannf.) L81. T. Shen (C. pilosula var. modesta) and Codonopsis tangshen Oliv (C. tangshen). As major constituents of CR, oligosaccharides might contribute to its clinical efficacy except for other known active compounds, yet the differences in the oligosaccharide profiles of these three varieties of CR remain incompletely understood. In the present study, 135 samples from these different CR varieties were harvested, and oligosaccharide fingerprints for these samples were characterized via HPLC-ELSD, with 19 common peaks being matched. Oligosaccharides were further identified through the combination of electrospray ionization MS/MS (ESI-MS/MS) with nuclear magnetic resonance (NMR). Principal component analysis and linear discriminant analysis (LDA) approaches were then used to compare the oligosaccharide profiles of these three CR varieties. These analyses ultimately revealed that CR was compared with principal component analysis (PCA) and linear discriminant (LDA) methods. The analyses ultimately revealed that these CR samples contained high levels of inulin- and levan-type fructooligosaccharides (FOS), with variations in the relative levels of these FOS compounds among the three analyzed CR varieties. Through the combined analysis of oligosaccharide fingerprints and LDA results, it was possible to differentiate among these CR varieties, with an accurate classification rate of 96.3% and a cross-validation rate of 95.6%. Together, these results highlight a valuable approach to the classification and identification of different CR varieties.
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