Intra-regional classification and quality evaluation of honey from Mendoza (Argentina) based on multi-elemental analysis and chemometrics

Abstract

Multi-elemental analysis of honey samples from Mendoza (Argentina) was performed with the aim of developing a reliable method for tracing honey provenance. The concentrations of twenty-six elements (Li, Na, Mg, Al, Ca, Ti, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, As, Se, Rb, Sr, Mo, Pd, Ag, Cd, Sn, Sb, Hg and Pb) were determined by inductively coupled plasma mass spectrometry (ICP-MS), considering the most abundant isotopes. Subsequently, a comparative machine learning approach for classification and for variable selection was applied to evaluate the possibility of using them as relevant markers to predict the region where honey was produced. Our results clearly demonstrate the potential of decision tree classifiers, such as Random Forest (RF), C5.0, recursive partitioning (rpart) and conditional inference tree (ctree), as simple and agile chemometric tools for honey origin identification. Moreover, the variable selection tools reduced the elemental data matrix to only six elements (Co, Sr, Zn, Na, Rb and Li) which were identified as the most important for predicting honey origin.