Abstract
The aim of the present study was: (1) to determine the mineral content of pine and thyme honeys produced in different regions in Greece and (2) differentiate pine and thyme honeys according to their geographical origin, based on selected minerals using multivariate analysis of variance (MANOVA) and linear discriminant analysis (LDA). For this purpose, 39 pine and 42 honey samples were collected from 9 different regions in Greece known to produce such types of honey. Twenty five minerals (Ag, Al, As, B, Ba, Be, Ca, Cd, Co, Cr, Cu, Fe, Hg, Mg, Mn, Mo, Ni, Pb, Sb, Se, Si, Ti, Tl, V, and Zn) were quantified using inductively coupled plasma-atomic emission spectroscopy. Results showed that pine and thyme honeys recorded variations in their mineral content according to geographical origin. A comparison between the two honey types revealed that pine honeys possessed 92.5 % higher total mineral content, compared to thyme honeys. This was also documented, by measuring electrical conductivity of pine and thyme honeys (mean ± SD values of 1.10 ± 0.27 and 0.40 ± 0.05, respectively) which gave an excellent Pearson’s correlation (r = 1) with their total mineral content. In addition, applying MANOVA and LDA to the mineral content of each honey type, honeys were satisfactorily correctly classified according to geographical origin. The correct prediction rates were 84.6 and 83.3 % for pine and thyme honeys, respectively, using the cross-validation method. Present results showed that selected minerals in combination with chemometrics may aid to the geographical differentiation of Greek honeys.
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Acknowledgments
The authors are grateful to Dr. Sofia Karabournioti for her excellent assistance in the melissopalynological analysis of honey samples, Attiki honey S.A, Athens, and to local beekeepers from Symi and Lakonia, for the donation of honey samples.
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Karabagias, I.K., Louppis, A.P., Kontakos, S. et al. Characterization and geographical discrimination of Greek pine and thyme honeys based on their mineral content, using chemometrics. Eur Food Res Technol 243, 101–113 (2017). https://doi.org/10.1007/s00217-016-2727-8
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DOI: https://doi.org/10.1007/s00217-016-2727-8