This technology employs an electronic tongue system that combines electrochemical sensors with multivariate data analysis to classify and differentiate genuine honey from certain types of adulterated honey. The system uses cyclic voltammetry with three different working electrodes (carbon, platinum, and gold) to acquire unique electrochemical signals, which are then analyzed using machine learning methods such as k-Nearest Neighbors (k-NN). This approach ensures high accuracy in detecting adulteration by identifying electrochemical patterns that distinguish pure honey from adulterated variants.