Geographical origin and botanical type honey authentication through elemental metabolomics via chemometrics. (15th February 2021) - Drivelos, Spiros A.; Danezis, Georgios P.; Halagarda, Michał; Popek, Stanisław; Georgiou, Constantinos A.
Highlights: Honey authenticity assessment via elemental metabolomics. Discriminant Analysis for geographical origin & botanical type authentication. Probabilistic Neural Networks classified 85.3% of samples on geographical origin. Partial Least Squares gave geographical origin prediction accuracy >95%. Abstract: 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.