Low-cost rapid workflow for honey adulteration detection by UV–Vis spectroscopy in combination with factorial design, response surface methodology and supervised machine learning classifiers

Ultraviolet–visible spectroscopy combined with machine learning as a rapid detection method to the predict adulteration of honey

“…From the studies that have been conducted using UV–Vis spectroscopy on honey, the authentication and classification of honeys according to their botanical, entomological, and geographical origins using ultraviolet (UV) spectroscopy and SIMCA (soft independent modeling of class analogy) [ 25 ], honey adulteration detection by UV–Vis spectroscopy in combination with factorial design, response surface methodology and supervised machine learning classifiers [ 26 ], UV–Vis spectroscopy and Raman spectroscopy together with principal components analysis [ 18 ], UV–Visible spectroscopy coupled with linear discrimination analysis to discriminate between monofloral and multifloral honey [ 27 ], Classification of the Botanical Origin for Honey [ 28 ], characterization of Brazilian floral honey using UV–vis, near-infrared (NIR), and nuclear magnetic resonance (NMR) spectroscopy [ 29 ], and honey authentication with sugar syrups using UV–Vis spectroscopy and one-class classifiers [ 2 ] can be mentioned. What is certain is that UV–Vis spectroscopy was not used alone in any of these studies to detect honey adulteration, and used along with other equipment.…”