Quantitative determination of compounds from akebia quinata by high-performance liquid chromatography

  • Nguyen Thi Yen
  • , Nguyen Van Thu
  • , Bing Tian Zhao
  • , Jae Hyun Lee
  • , Jeong Ah Kim
  • , Jong Keun Son
  • , Jae Sui Choi
  • , Eun Rhan Woo
  • , Mi Hee Woo
  • , Byung Sun Min

Research output: Contribution to journalArticlepeer-review

8 Scopus citations

Abstract

To provide the scientific corroboration of the traditional uses of Akebia quinata (Thunb.) Decne., a detailed analytical examination of A. quinata stems was carried out using a reversed-phase high performance liquid chromatography (RP-HPLC) method coupled to photodiode array detector (PDA) for the simultaneous determination of four phenolic substances; cuneataside D (1), 2-(3,4-dihydroxyphenyl)ethyl-O-β-D-glucopyranoside (2), 3-caffeoylquinic acid (3) and calceolarioside B (4). Particular attention was focused on the main compound, 3-caffeoylquinic acid (3), which has a range of biological functions. In addition, 2-(3,4- dihydroxyphenyl)ethyl-O-β-D-glucopyranoside (2) was considered as a discernible marker of A. quinata from its easy confuse plants. The contents of compounds 2 and 3 ranged from 0.72 to 2.68 mg/g and from 1.66 to 5.64 mg/g, respectively. The validation data indicated that this HPLC/PDA assay was used successfully to quantify the four phenolic compounds in A. quinata from different locations using relatively simple conditions and procedures. The pattern-recognition analysis data from 53 samples classified them into two groups, allowing discrimination between A. quinata and comparable herbs. The results suggest that the established HPLC/PDA method is suitable for quantitation and pattern-recognition analyses for a quality evaluation of this medicinal herb.

Original languageEnglish
Pages (from-to)1956-1964
Number of pages9
JournalBulletin of the Korean Chemical Society
Volume35
Issue number7
DOIs
StatePublished - 20 Jul 2014

Keywords

  • 3-Caffeoylquinic acid
  • Akebia quinata
  • HPLC
  • Lardizabalaceae
  • Pattern-recognition analyses

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