Vol 8, No 3 (2017) > Electrical, Electronics and Computer Engineering >

Development of Rapid and Accurate Method to Classify Malaysian Honey Samples using UV and Colour Image

Abd Alazeez Almaleeh, Abdul Hamid Adom, Ammar Zakaria


Abstract: The purpose of this
paper is to classification of three main types of Malaysian honey (Acacia,
Kelulut and Tualang) according to their botanical origin using UV–Vis
Spectroscopy and digital camera. This paper presented the classification of the
honey based on two characteristics from
three (3) types of local honey,
namely the antioxidant contents and colour variations.  The former uses the UV spectroscopy of
selected wavelength range, and the latter using RGB digital camera. Principal
Component Analysis (PCA) was used for both methods to reduce the dimension of
extracted data. The Support Vector Machine (SVM) was used for the
classification of honey. The assessment was done separately for each of the
methods, and also on the fusion of both data after features extraction and
association. This paper shows that classification of the fusion method improved
significantly compared to single modality Honey classification based on the fusion method was able to achieve 94%
accuracy. Hence, the proposed methods have the ability to
provide accurate and rapid classification of honey products in terms of origin.
The proposed system can be applied in Malaysia honey industry and further improve the
quality assessment and provide traceability.
Keywords: Data fusion; Honey classification; Sensors; Support Vector Machine

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