Classification System of Honey Floral Origin based on Visual Near-Infrared Imaging

Abstract

Honey is a sweet, sticky, yellowish-brown fluid made by bees and other insects from nectar collected from flowers, which often been used for food supplement or natural drug. Each nectar flower produces different kind of honey, for each honey has particular benefits. In this study, a detection system of honey botanical origin was proposed based on the spectral transmittance profile using hyperspectral imaging and machine learning. An acquiring image system consists of the transmittance module, halogen lamp, object slider, and hyperspectral imaging system. The image was recorded in 448 bands with a wavelength range from 400 nm to 1000 nm. An image processing method performs image correction, segmentation, feature extraction, feature reduction, and classification model. A classification model used a Pattern Recognition Network with a single hidden layer. A Bayesian regularization backpropagation was conducted to train the model. Five-type of the honey botanical origin from three different brands was collected to evaluate the proposed system. Three samples were prepared and measured for each botanical from each brand to create the honey dataset. A confusion matrix was used to measure classification performance. Based on the experiment result, the accuracy of botanical origin classification is 94.1%. The result shows an excellent result for the classification system.