Classification of Andrographis paniculata extracts by solvent extraction using HPLC fingerprint and chemometric analysis

M. Rafi, A. F. Devi, U. D. Syafitri, R. Heryanto, I. H. Suparto, M. B. Amran, A. Rohman, B. Prajogo, L. W. Lim

Research output: Contribution to journalArticlepeer-review

28 Citations (Scopus)

Abstract

Objective: Andrographis paniculata, widely used as an antidiabetic in Indonesian traditional medicines (jamu), contains chemical compounds whose concentration is related to its therapeutic effects. The concentration of solvents used for extraction will also affect the number of compounds extracted. Therefore, a quality control method is needed to ensure consistency in quantifying these compounds in A. paniculata to improve its therapeutic application. High-performance liquid chromatography fingerprint analysis combined with chemometrics was used to evaluate extracts from different solvent extraction treatments. The content of andrographolide, the main bioactive compound in A. paniculata, and the level of α-glucosidase inhibition activity, an indicator of its antidiabetic activity, were also determined. Results: Fingerprint chromatograms of A. paniculata extracts from different treatments exhibited a similar pattern with several peaks in common, only differing in area and intensity value. The A. paniculata extracts were classified using HPLC fingerprint and principal component analysis to allow grouping according to their respective solvent extraction treatments. The highest andrographolide content and α-glucosidase inhibition activity occurred in the 50% ethanol extract and the lowest in the water extract. HPLC fingerprint analysis could be used for identifying A. paniculata extracts based on solvent extraction, thus improving quality control for their therapeutic application.

Original languageEnglish
Article number56
JournalBMC Research Notes
Volume13
Issue number1
DOIs
Publication statusPublished - 4 Feb 2020

Keywords

  • A. paniculata
  • Classification
  • HPLC fingerprint analysis
  • Principal component analysis

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