Expand this Topic clickable element to expand a topic
Skip to content
Optica Publishing Group
  • Applied Spectroscopy
  • Vol. 63,
  • Issue 6,
  • pp. 716-726
  • (2009)

A Rapid Technique for Classifying Phytoplankton Fluorescence Spectra Based on Self-Organizing Maps

Not Accessible

Your library or personal account may give you access

Abstract

Fluorescence spectroscopy has been demonstrated to be a powerful tool for characterizing phytoplankton communities in marine environments. Using different fluorescence spectra techniques, it is now possible to discriminate the major phytoplankton groups. However, most of the current techniques are based on fluorescence excitation measurements, which require stimulation at different wavelengths and thus considerable time to obtain the complete spectral profile. This requirement may be an important constraint for several mobile oceanographic platforms, such as vertical profilers or autonomous underwater vehicles, which require rapid-acquisition instruments. This paper presents a novel technique for classifying fluorescence spectra based on self-organizing maps (SOMs), one of the most popular artificial neural network (ANN) methods. The method is able to achieve phytoplankton discrimination using only fluorescence emission spectra (single wavelength excitation), thus reducing the acquisition time. The discrimination capabilities of SOM using excitation and emission spectra are compared. The analysis shows that the SOM has a good performance using excitation spectra, whereas data preprocessing is required in order to obtain similar discrimination capabilities using emission spectra. The final results obtained using emission spectra indicate that the discrimination is properly achieved even between algal groups, such as diatoms and dinoflagellates, which cannot be discriminated with previous methods. We finally point out that although techniques based on excitation spectra can achieve a better taxonomic accuracy, there are some applications that require faster acquisition processes. Acquiring emission spectra is almost instantaneous, and techniques such as SOM can achieve good classification performance using appropriately preprocessed data.

PDF Article
More Like This
Statistical analysis of a database of absorption spectra of phytoplankton and pigment concentrations using self-organizing maps

Aymeric Chazottes, Annick Bricaud, Michel Crépon, and Sylvie Thiria
Appl. Opt. 45(31) 8102-8115 (2006)

Retrievals of phytoplankton community structures from in situ fluorescence measurements by HS-6P

Zunbin Ling, Deyong Sun, Shengqiang Wang, Zhongfeng Qiu, Yu Huan, Zhihua Mao, and Yijun He
Opt. Express 26(23) 30556-30575 (2018)

Statistical approach for the retrieval of phytoplankton community structures from in situ fluorescence measurements

Shengqiang Wang, Cong Xiao, Joji Ishizaka, Zhongfeng Qiu, Deyong Sun, Qian Xu, Yuanli Zhu, Yu Huan, and Yuji Watanabe
Opt. Express 24(21) 23635-23653 (2016)

Cited By

You do not have subscription access to this journal. Cited by links are available to subscribers only. You may subscribe either as an Optica member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access Optica Member Subscription

Select as filters


Select Topics Cancel
© Copyright 2024 | Optica Publishing Group. All Rights Reserved