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.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.