The development of green analytical methods to monitor adulteration in honey by UV-visible spectroscopy and chemometrics models

¹ Laboratory of Analytical Chemistry & Bromatology, Team of Formulation and Quality Control of Health Products Faculty of Medicine and Pharmacy, Mohammed V University Rabat, Morocco
² Laboratory of Chemical Processes and Applied Materials University of Sultan Moulay Slimane BeniMellal, Morocco
³ Faculty of Pharmacy, Abulcasis University Rabat, Morocco

^* Corresponding author: omar.elhamdaaoui@gmail.com

Abstract

The development of green and environmentally friendly analytical methods for agri-food products is an essential element to be treated by green analytical chemistry. In this study, UV-Visible spectroscopy, combined with a mathematical and statistical or chemometrics algorithm, has been developed to monitor honey quality. Partial Least Squares Regression (PLS-R) and Support Vector Machine Learning Regression (SVM-R) showed an adequate quantification of the percentage of impurity. The use of these models demonstrates a high ability to predict the quality of honey. R-square’s high value shows this ability, and the low value of root mean square error of calibration and cross-validation (RMSECV, RMSEC). The results indicate that UV-Visible spectroscopy allied with the Chemometrics algorithms can provide a quick, non-destructive, green, and reliable method to control the quality and predict honey’s adulteration level.