Literature DB >> 19147117

Classification of pollen species using autofluorescence image analysis.

Kotaro Mitsumoto1, Katsumi Yabusaki, Hideki Aoyagi.   

Abstract

A new method to classify pollen species was developed by monitoring autofluorescence images of pollen grains. The pollens of nine species were selected, and their autofluorescence images were captured by a microscope equipped with a digital camera. The pollen size and the ratio of the blue to red pollen autofluorescence spectra (the B/R ratio) were calculated by image processing. The B/R ratios and pollen size varied among the species. Furthermore, the scatter-plot of pollen size versus the B/R ratio showed that pollen could be classified to the species level using both parameters. The pollen size and B/R ratio were confirmed by means of particle flow image analysis and the fluorescence spectra, respectively. These results suggest that a flow system capable of measuring both scattered light and the autofluorescence of particles could classify and count pollen grains in real time.

Mesh:

Substances:

Year:  2009        PMID: 19147117     DOI: 10.1016/j.jbiosc.2008.10.001

Source DB:  PubMed          Journal:  J Biosci Bioeng        ISSN: 1347-4421            Impact factor:   2.894


  5 in total

1.  Multivariate Analysis of MALDI Imaging Mass Spectrometry Data of Mixtures of Single Pollen Grains.

Authors:  Franziska Lauer; Sabrina Diehn; Stephan Seifert; Janina Kneipp; Volker Sauerland; Cesar Barahona; Steffen Weidner
Journal:  J Am Soc Mass Spectrom       Date:  2018-07-24       Impact factor: 3.109

Review 2.  Immunologic, spectrophotometric and nucleic acid based methods for the detection and quantification of airborne pollen.

Authors:  William R Rittenour; Robert G Hamilton; Donald H Beezhold; Brett J Green
Journal:  J Immunol Methods       Date:  2012-02-03       Impact factor: 2.303

3.  Fullerene fine particles adhere to pollen grains and affect their autofluorescence and germination.

Authors:  Hideki Aoyagi; Charles U Ugwu
Journal:  Nanotechnol Sci Appl       Date:  2011-05-20

4.  Easier detection of invertebrate "identification-key characters" with light of different wavelengths.

Authors:  Marcel Hm Koken; Jacques Grall
Journal:  Front Zool       Date:  2011-10-31       Impact factor: 3.172

5.  A Novel Method for the Separation of Overlapping Pollen Species for Automated Detection and Classification.

Authors:  Santiago Tello-Mijares; Francisco Flores
Journal:  Comput Math Methods Med       Date:  2016-03-10       Impact factor: 2.238

  5 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.