Colour classification of Bulgarian honey using spectroradiometry and neural network processing

University of Food Technologies, dpt. Of Electrical engineering, electronics and automation, 26, Maritsa Blvd, Plovdiv, Bulgaria

^* Corresponding author: This email address is being protected from spambots. You need JavaScript enabled to view it.

Abstract

This publication explores the possibility of implementing a colour classifier for various types of Bulgarian honey. For this purpose, spectroradiometric measurement is used based on a measuring device from JETI Technische Instrumente GmbH – Specbos1201 providing a measurement range in the visible spectrum – 380 ~ 820 nm, which is complemented by a reference light source BYK Byko-spectra – D65. The obtained data is used to implement two types of classification, between which a comparison was made in terms of a quality indicator - a KNN classifier and a classifier with a neural network with discrete outputs. In order to reduce the number of inputs, the spectral data was transformed into the standard CIE-Lab 1976 colour space. The obtained results show a very good possibility for implementing a classifier using a neural network, with an extremely low level of potential misclassification despite the usage of a relatively small amount of training data.