Classification of Monofloral Honeys by Measuring Electrical Impedance Based on Neural Networks

Abstract

The study of electrical impedance applied to food has become a method with great potential for use in the food industry, which allows the monitoring and control of quality processes in a safe and non-invasive way. Recent research has shown that this technique can be an alternative method to determine the floral origin of the honey bee (Apis mellifera L.) and acquire information on chemical and physical properties such as conductivity, ash content and acidity. In this work, the electrical impedance of six monofloral honey samples from diverse origins and one commercial multi-floral honey were measured using a low-cost impedance meter, obtaining 101 samples (reactance (X) versus resistance (R)), with a frequency sweep between 1 Hz and 25 MHz in all the honeys analyzed. This shows that it is possible, by using a multilayer neural network trained from these data, to classify with 100% accuracy between these honeys and, thereby, quickly and easily determine the floral origin of the honey. This is without the need to use the chemical data or equivalent electrical models.