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Optical Engineering

Classification of bee pollen grains using hyperspectral microscopy imaging and Fisher linear classifier
Author(s): Kang Su; Siqi Zhu; Lin Wei; Zhen Li; Hao Yin; Pingping Ye; Anming Li; Zhenqiang Chen; Migao Li
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Paper Abstract

The rapid and accurate classification of bee pollen grains is still a challenge. The purpose of this paper is to develop a method which could directly classify bee pollen grains based on fluorescence spectra. Bee pollen grain samples of six species were excited by a 409-nm laser diode source, and their fluorescence images were acquired by a hyperspectral microscopy imaging (HMI) system. One hundred pixels in the region of interest were randomly selected from each single bee pollen species. The fluorescence spectral information in all the selected pixels was stored in an n-dimensional hyperspectral data set, where n=37 for a total of 37 hyperspectral bands (465 to 645 nm). The hyperspectral data set was classified using a Fisher linear classifier. The performance of the Fisher linear classifier was measured by the leave-one-out cross-validation method, which yielded an overall accuracy of 89.2%. Finally, additional blinded samples were used to evaluate the established classification model, which demonstrated that bee pollen mixtures could be classified efficiently with the HMI system.

Paper Details

Date Published: 5 May 2016
PDF: 7 pages
Opt. Eng. 55(5) 053102 doi: 10.1117/1.OE.55.5.053102
Published in: Optical Engineering Volume 55, Issue 5
Show Author Affiliations
Kang Su, Jinan Univ. (China)
Siqi Zhu, Jinan Univ. (China)
Lin Wei, Jinan Univ. (China)
Zhen Li, Jinan Univ. (China)
Hao Yin, Jinan Univ. (China)
Pingping Ye, Jinan Univ. (China)
Anming Li, Jinan Univ. (China)
Zhenqiang Chen, Jinan Univ. (China)
Migao Li, Guangzhou Liss Optical Instrument Co., Ltd. (China)


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