Development of Clustering and Classification Strategies for the Determination of Geographical Origin of Honey by Using Atomic and Molecular Spectrometry

Abstract

Honey is a natural, nutritious and healthy food produced by honeybees from the nectar of plants. The classification of honey based on geographical origin is of great interest since the quality of honey depends on its chemical composition and geographical origin. In this study, it is aimed to develop classification models using elemental and molecular composition of honey samples via atomic and molecular spectrometry. For this purpose, honey samples from different regions of Turkey were collected from producers and they were scanned with Fourier Transform infrared spectrometer equipped with attenuated total reflectance (FTIR-ATR) accessory, and fluorescence spectrophotometer (synchronous fluorescence mode and 3D excitation emission mode). Afterwards, any clustering of the samples based on their regions was investigated using principal component analysis (PCA) and hierarchical cluster analysis (HCA) and soft independent modeling of class analogies (SIMCA). Finally, inductively coupled plasma mass spectrometry was applied to determine the metal concentrations (Mg, Al, Mn, Fe, Co, Ni, Cu, Zn, Sr, Ba) in honey samples and then the same classification methods were performed to compare the results. In conclusion, molecular spectrometry gave better classification results based on geographical origin compared to the results obtained with atomic spectrometry. Molecular spectrometry is more advantageous for the classification of honey samples in the case of saving time, saving chemicals and ease of usage.