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Plant Metabolomics as a Tool for Detecting Adulterants in Edible Plant: A Case Study of Allium ursinum .

Stefan IvanovićKatarina SimićStefan LekićMilka JadraninLjubodrag VujisićDejan Gođevac
Published in: Metabolites (2022)
Allium ursinum and poisonous adulterants Convallaria majalis and Arum maculatum were used as a model for detection of adulterants in edible plant. A. ursinum samples were spiked with C. majalis and A. maculatum to mimic adulteration. Metabolomic fingerprinting of all samples was performed using 1 H NMR spectroscopy, and the resulting data sets were subjected to multivariate data analysis. As a result of this analysis, signals of adulterants were extracted from the data, and the structures of biomarkers of adulteration from partially purified samples were elucidated using 2D NMR and LC-MS techniques. Thus, isovitexin and vicenin II, azetidine-2-carboxylic acid, and trigonelline indicated adulteration of A. ursinum samples with C. majalis . Isovitexin was also recognized to be an indicator of adulteration of A. ursinum with A. maculatum . In conclusion, the case study of A. ursinum suggested that plant metabolomics approach could be utilized for identification of low molecular weight biomarkers of adulteration in edible plants.
Keyphrases
  • data analysis
  • mass spectrometry
  • high resolution
  • electronic health record
  • magnetic resonance
  • cell wall
  • big data
  • machine learning
  • plant growth
  • artificial intelligence
  • sensitive detection