Table of Contents
ISRN Spectroscopy
Volume 2012, Article ID 487040, 4 pages
Research Article

Honey Discrimination Using Visible and Near-Infrared Spectroscopy

College of Information Engineering, Zhejiang University of Technology, Hangzhou 310032, China

Received 24 August 2012; Accepted 9 October 2012

Academic Editors: R. Fausto, J. Ghasemi, and H. C. Goicoechea

Copyright © 2012 Yun Li and Haiqing Yang. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


This study aims to investigate the potential of honey discrimination by visible and near-infrared (vis-NIR) spectroscopy with wavelength reduction. A total of 80 samples from four brands of honey produces were measured by a mobile fiber-type USB4000 spectrophotometer with recorded wavelength range of 380.17~939.98 nm for model calibration. Firstly, principal components analysis (PCA) was used for extracting principal components (PCs). Next, the first seven PCs, which accounted for 97% of variance of the spectra, were combined separately with support vector machine (SVM) and linear discriminate analysis (LDA) to develop PC-SVM and PC-LDA models, both of which achieved 100% discrimination accuracy. In addition, the spectra were subjected to successive wavelength reduction rates (WRRs) of 2x, x = 1–9, for wavelength reduction. The PC-LDA and PC-SVM models developed for these reduced wavelengths produced almost the same performance as compared with those developed for original full wavelengths. This experiment suggests that vis-NIR spectral wavelengths can be reduced at large spacing interval, which allows easing data analysis as well as developing a simpler and cheaper sensor for honey discrimination in practice.