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Assessment of Polygonum capitatum Buch.‐Ham. ex D.Don by metabolomics based on gas chromatography with mass spectrometry
Author(s) -
Han Pei,
Huang Yong,
Hylands Peter J.,
LegidoQuigley Cristina
Publication year - 2016
Publication title -
journal of separation science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.72
H-Index - 102
eISSN - 1615-9314
pISSN - 1615-9306
DOI - 10.1002/jssc.201501331
Subject(s) - chromatography , mass spectrometry , hierarchical clustering , gas chromatography–mass spectrometry , chemistry , metabolomics , polygonum , gas chromatography , chemometrics , mathematics , cluster analysis , statistics , traditional medicine , medicine
Polygonum capitatum is widely used in southwest China. It has considerable therapeutic efficacy for urinary tract infections. P. capitatum contains multiple components and quality assessment can be achieved by means of metabolic fingerprinting. In this paper, a new strategy for P. capitatum quality determination was developed. Eleven batches of P. capitatum were collected from five geographical areas in China including a standard batch regulated by Good Agriculture Practice. Gas chromatography with mass spectrometry was used to generate fingerprints from triplicate extractions to each batch ( n = 33). Hierarchical clustering analysis was applied to assess similarities among the ten batches to the standard batches. Orthogonal projection to latent structures discriminate analysis, cross‐validated with permutation tests, was performed to investigate discriminating metabolites. Results demonstrated that the overall evaluation hierarchical clustering analysis clustered two batches with distance > 3. Orthogonal projection to latent structures discriminate analysis ( R 2 Y (cum) = 0.997, Q 2 (cum) = 0.97, CV‐ANOVA = 8.48 × 10 −11 ) indicated that several sugars contributed to batch classification. This method is a rational approach that can classify against a regulated plant standard and distinguishes samples from different origins or processing time in a holistic manner and metabolites driving any differences can be easily identified.