Abstract
Lycii Fructus is a traditional medicine used to prevent liver and kidney diseases, which commonly derives from Lycium chinense and Lycium barbarum. Here, the extracts and ethyl acetate-soluble fractions of L. chinense fruits exhibited better hepatoprotective effects than those of L. barbarum, which was likely due to differences in their composition. Therefore, GC-MS and HPLC analyses were conducted to characterize the metabolite differences between L. chinense and L. barbarum. Based on amino acid (AA) and phenolic acid (PA) profiling, 24 AAs and 9 PAs were identified in the two species. Moreover, each species exhibited unique and readily distinguishable AA and PA star graphic patterns. HPLC analysis elucidated composition differences between the ethyl acetate-soluble layers of the two compounds. Further, NMR analysis identified their chemical structures as 4-(2-formyl-5-(hydroxymethyl)-1H-pyrrol-1-yl)butanoic acid and p-coumaric acid. The higher content of 4-(2-formyl-5-(hydroxymethyl)-1H-pyrrol-1-yl)butanoic acid was detected in L. chinense, whereas the content of p-coumaric acid was higher in L. barbarum. Therefore, the differences in the relative contents of these two secondary metabolites in the ethyl acetate-soluble layer of Lycii Fructus could be a good marker to discriminate between L. chinense and L. barbarum.
Original language | English |
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Article number | 5835 |
Journal | Molecules |
Volume | 25 |
Issue number | 24 |
DOIs | |
State | Published - 1 Dec 2020 |
Bibliographical note
Funding Information:Funding: This research was supported by a grant from the Ministry of Trade, Industry & Energy (MOTIE), Korea Institute for Advancement of Technology (KIAT) through the Encouragement Program for The Industries of the Economic Cooperation Region and the National Research Foundation of Korea (2018R1D1A1B07042919 and 2018R1A5A2025286).
Publisher Copyright:
© 2020 by the authors. Licensee MDPI, Basel, Switzerland.
Keywords
- Lycium barbarum
- Lycium chinense
- gas chromatography-mass spectrometry
- star pattern recognition analysis