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

Food Chem. 2021 Feb 15:338:127936. doi: 10.1016/j.foodchem.2020.127936. Epub 2020 Aug 25.

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.

Keywords: Authenticity; Botanical type; Chemometrics; Elemental metabolomics; Geographical origin; Honey; Rare earth elements.

MeSH terms

  • Cheminformatics*
  • Cluster Analysis
  • Discriminant Analysis
  • Fraud / prevention & control
  • Geography*
  • Honey / analysis*
  • Least-Squares Analysis
  • Metabolomics*
  • Metals, Rare Earth / analysis
  • Neural Networks, Computer
  • Spectrum Analysis
  • Trace Elements / analysis

Substances

  • Metals, Rare Earth
  • Trace Elements