Design and manufacturing a microcontroller based measurement device for honey adulteration detection

The aim of this study is the detection of adulteration in honey by a microcontroller measurement device. For this purpose, 18 pure honey samples from Sidr, Locoweed and Citrus honey were prepared, whose physicochemical characteristics, including moisture content and ⁰Brix, pH, color and ash content were measured. To detect impurities in honey, electrical conductivity and light refraction were used as inputs of an artificial neural network (ANN) classifier. This allowed fraud detection in honey by a portable instrument developed by enclosing the electrical conductivity and light sensors connected to a smartphone application. The results revealed that increasing impurities caused a decrease in ⁰Brix and an increase in the pH of honey. The highest value of moisture was 17.5% (for Locoweed) and the highest value of pH was 4.69 (for Sidr) at 75% impurity concentration. Citrus showed the highest value of ash and electrical conductivity of 0.36 and 415 ppm, respectively. The lowest error of training the ANN classifier of 13% was obtained with 14 neurons as the optimal number of neurons in the hidden layer. The honey samples with fructose concentrations of 0% (pure honey), 10%, 25%, 50%, 75% and 100% (pure fructose) were successfully classified, with correct classification rates of 100%, 83.3%, 80%, 87.5%, 100% and 100%, respectively. This reveals the acceptable performance of the designed device in detecting honey adulteration