S Minaei, S Shafiee, G Polder, N Moghadam-Charkari… – Infrared Physics & Technology, 2017

Authors
Saeid Minaei, Sahameh Shafiee, Gerrit Polder, Nasrolah Moghadam-Charkari, Saskia van Ruth, Mohsen Barzegar, Javad Zahiri, Martin Alewijn, Piotr M Kuś
Publication date
2017/9/6
Source
Infrared Physics & Technology
Publisher
Pergamon
Description
Nondestructive methods are of utmost importance for honey characterization. This study investigates the potential application of VIS-NIR hyperspectral imaging for detection of honey flower origin detection using machine learning techniques. Hyperspectral images of 52 honey samples were taken in transmittance mode in the visible/near infrared (VIS-NIR) range (400-1000 nm). Three different machine learning algorithms were implemented to predict honey floral origin using honey spectral images. These methods, included radial basis function (RBF) network, support vector machine (SVM), and random forest (RF). Principal component analysis (PCA) was also exploited for dimensionality reduction. According to the obtained results, the best classifier (RBF) achieved a precision of 94% in a five-fold cross validation experiment using only the first two PCs. Mapping of the classifier results to the test set images showed …

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