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Patterns of Variation and Chemosystematic Significance of Phenolic Compounds in the Genus Cyclopia (Fabaceae, Podalyrieae).

Marietjie A StanderHerman RedelinghuysKeabetswe MasikeHelen LongBen-Erik Van Wyk
Published in: Molecules (Basel, Switzerland) (2019)
As a contribution towards a better understanding of phenolic variation in the genus Cyclopia (honeybush tea), a collection of 82 samples from 15 of the 23 known species was analysed using liquid-chromatography-high resolution mass spectrometry (UPLC-HRMS) in electrospray ionization (ESI) negative mode. Mangiferin and isomangiferin were found to be the main compounds detected in most samples, with the exception of C. bowiena and C. buxifolia where none of these compounds were detected. These xanthones were found to be absent from the seeds and also illustrated consistent differences between species and provenances. Results for contemporary samples agreed closely with those based on analysis of a collection of ca. 30-year-old samples. The use of multivariate tools allowed for graphical visualizations of the patterns of variation as well as the levels of the main phenolic compounds. Exclusion of mangiferin and citric acid from the data was found to give better visual separation between species. The use of UPLC-HRMS generated a large dataset that allowed for comparisons between species, provenances and plant parts (leaves, pods, flowers and seeds). Phenetic analyses resulted in groupings of samples that were partly congruent with species but not with morphological groupings within the genus. Although different provenances of the same species were sometimes found to be very variable, Principle Component Analysis (PCA) indicated that a combination of compounds have some (albeit limited) potential as diagnostic characters at species level. 74 Phenolic compounds are presented, many of which were identified for the first time in Cyclopia species, with nine of these being responsible for the separation between samples in the PCAs.
Keyphrases
  • liquid chromatography
  • high resolution mass spectrometry
  • mass spectrometry
  • genetic diversity
  • simultaneous determination
  • high resolution
  • deep learning
  • data analysis
  • plant growth