Discrimination of honey of different floral origins by a combination of various chemical parameters

Z. Jandrić*, S. A. Haughey, R. D. Frew, K. McComb, P. Galvin-King, C. T. Elliott, A. Cannavan

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

81 Citations (Scopus)


Abstract Honey is a high value food commodity with recognized nutraceutical properties. A primary driver of the value of honey is its floral origin. The feasibility of applying multivariate data analysis to various chemical parameters for the discrimination of honeys was explored. This approach was applied to four authentic honeys with different floral origins (rata, kamahi, clover and manuka) obtained from producers in New Zealand. Results from elemental profiling, stable isotope analysis, metabolomics (UPLC-QToF MS), and NIR, FT-IR, and Raman spectroscopic fingerprinting were analyzed. Orthogonal partial least square discriminant analysis (OPLS-DA) was used to determine which technique or combination of techniques provided the best classification and prediction abilities. Good prediction values were achieved using metabolite data (for all four honeys, Q2 = 0.52; for manuka and clover, Q2 = 0.76) and the trace element/isotopic data (for manuka and clover, Q2 = 0.65), while the other chemical parameters showed promise when combined (for manuka and clover, Q2 = 0.43).

Original languageEnglish
Pages (from-to)52-59
Number of pages8
JournalFood Chemistry
Early online date07 Feb 2015
Publication statusPublished - 15 Dec 2015


  • Chemometrics
  • Discrimination
  • Elemental profiling
  • Fingerprinting
  • Honey
  • Metabolomics
  • Spectroscopy

ASJC Scopus subject areas

  • Food Science
  • Analytical Chemistry


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