Honey discrimination based on the bee feeding by Laser Induced Breakdown Spectroscopy
2022
Stefas, Dimitrios | Gyftokostas, Nikolaos | Kourelias, Panagiotis | Nanou, Eleni | Tananaki, Chrysoula | Kanelis, Dimitrios | Liolios, Vasileios | Kokkinos, Vasileios | Bouras, C. (Christos) | Couris, Stelios
In the present work, Laser Induced Breakdown Spectroscopy (LIBS) is used for the first time to investigate the effects of artificial feeding of bees on the honey. According to LIBS technique the emission spectral characteristics of the plasma created on the surface of honey samples are analyzed. Correlation plots indicating the importance of spectral lines of elements as e.g., Calcium (Ca), Magnesium (Mg), Sodium (Na) and Potassium (K) are constructed. In addition, machine learning algorithms based on Linear Discriminant Analysis (LDA) and Random Forest Classifiers (RFC) are employed to classify the honey samples in terms of the bee food used. The constructed machine learning models were validated by both cross-validation and external validation, while the obtained accuracies exceeded 90% of correct classification, indicating the potential of LIBS technique for honey discrimination. The obtained results by LIBS were also validated by HPLC-RID, which is the standard technique used for the analysis of the main honey sugars.
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