Honey Discrimination Based On The Bee Feeding By Laser Induced Breakdown Spectroscopy

In the present work, the effects of artificial feeding of bees on the honey are investigated by employing for the first time, Laser Induced Breakdown Spectroscopy (LIBS) by analyzing the emission spectral characteristics of the plasma created on the surface of honey samples. Correlation plots indicating the importance of spectral lines of elements as e.g., Calcium (Ca), Magnesium (Mg), Sodium (Na) and Potassium (K) are constructed, while 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.