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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. [ABSTRACT FROM AUTHOR]

A diode-side-pumped actively Q-switched first-Stokes eye-safe laser with a Nd:YAP/YVO4 crystal is reported. Under the pump power of 250 W, a stable Raman laser at 1525 nm with an average output power of 4.5Wand pulse width of 60 ns at the pulse repetition frequency of 4.5 kHz is achieved. Another laser crystal Nd: YAG is used to carry out the contrast experiment. The output performances of these two lasers are measured and compared. The result reveals that the first-stokes Raman laser generated by the Nd:YAP/YVO4 crystal has a lower power threshold and higher Raman conversion efficiency because of the large value of the stimulated emission cross section and the fluorescence lifetime at 1342 nm of the Nd: YAP crystal. [ABSTRACT FROM AUTHOR]