Graphical Discrimination of New Zealand Honey from International Honey Using Elemental Analysis

Abstract

This study investigated the potential of utilising the elemental fingerprinting of honey to differentiate New Zealand (NZ) honey from that of international origin. Twenty elements were analysed by ICP-MS in 352 honeys from 34 various countries. Of these, 323 honeys (245 New Zealand honeys, 78 international) and two subsets of data (NZ and European origin, n = 306, and, NZ and Denmark/Germany, n = 280) were visualised using principal component analysis (PCA). For the NZ/Europe subset, 42.2% of data was explained in the first two principal components. Statistical classification rules were also derived using linear discriminant analysis (LDA) and decision tree analysis. Various combinations of elements were explored for classification, considering the effect of soil-derived elements and those from anthropogenic sources. A high degree of accuracy (at least 90%) for the characterisation of New Zealand honey was observed for all statistical models, showing the robustness of these analyses. When using decision tree analysis to distinguish New Zealand samples from international samples, a tree with five terminal nodes (using Cs, Ba and Rb) was created with 92.4% accuracy. This work has demonstrated that elemental fingerprints of honey are a promising tool for categorising New Zealand honey from other geographical locations.

Elemental Fingerprinting Combined with Machine Learning Techniques as a Powerful Tool for Geographical Discrimination of Honeys from Nearby Regions

“…The elemental composition of honey has been characterized worldwide. Recent studies investigated honeys from Argentina [48], Bulgaria [40,49,50], China [43,[51][52][53], Croatia [42,49,54,55], Denmark [56], Egypt [57], Eritrea [58], Ethiopia [59], France [60], Germany [56], Greece [37,57,61], Hungary [62,63], Israel [64], Italy [37,38,46,[65][66][67][68][69][70][71][72][73], Kazakhstan [73], Kosovo [74], Montenegro [75], Morocco [49,57], New Zealand [45,56], Poland [37], Romania [37,60], Serbia [74], Slovenia [49], Spain [57,...…”