Geographical origin and botanical type honey authentication through elemental metabolomics via chemometrics

The trace and rare earth elements content of 93 honeys of different botanical type and origin have been studied through ICP-MS. Discriminant Analysis (DA) was successful for botanical type and geographical origin classification while Cluster Analysis (CA) was successful only for botanical type. Through Probabilistic Neural Network (PNN) analysis, 85.3% were correctly classified by the network according to their geographical origin and 73.3% according to their organic characterization. A Partial Least Squares (PLS) model was constructed, giving a prediction accuracy of more than 95%. Information obtained using Rare Earths (Y, La, Ce, Pr, Nd, Sm, Eu, Gd, Tb, Dy, Ho, Er, Tm, Yb, Lu) and trace elements (Li, Mg, Mn, Ni, Co, Cu, Sr, Ba, Pb) via chemometric evaluation facilitated classification of honey samples.

Automated summary

This study analyzed the rare earth and trace element content of 93 honeys of different botanical types and origins using ICP-MS. Discriminant Analysis was successful in classifying both botanical type and geographical origin, while Cluster Analysis only worked for botanical type. Probabilistic Neural Network analysis correctly classified 85.3% of samples based on geographical origin and 73.3% based on organic characterization. A Partial Least Squares model was constructed with a prediction accuracy of over 95%, using information from rare earths and trace elements for classification of honey samples.