Honey Discrimination Using Visible and Near-Infrared Spectroscopy

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. 1. Introduction Honey is considered as healthy and wholesome food with curative properties as it contains plenty of nutrients and plays effective antimicrobial effects against many bacteria [1]. Honey produces are quite popular in China. Generally, consumers classify honeys in terms of color, smell, and taste. Although sensory evaluation is easy to use, subjective experience is often biased and low accurate. Using chemical test methods in laboratory may be accurate but quite time- and cost-consuming, which produces unsafe guarantee to ordinary consumers and hinders the development of apiculture. Thus, it is necessary to develop a convenient and accurate way for honey discrimination. Recently, visible and near-infrared (vis-NIR) spectroscopy has received wide attention as it is suitable for nondestructive analysis of biological and biomedical materials. For example, the vis-NIR spectroscopy can be used for discriminating tea beverages [2], coffee [3], milk power [4], and other materials [5–7]. Using this technique, Gallardo-Velázquez et al. [8] and Zhu et al. [9] qualified adulterants in some local origins of honey. Although these researches presented good accuracy for honey discrimination, the calibration models were developed using full range of wavelengths, which would result in high complexity in computation and cause difficulty in practical