Abstract
•Discrimination of honeys with different floral origins was achieved.•Classification models (OPLS-DA) were successfully validated using external data sets.•The most influential variables responsible for discrimination were identified.
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).