Identification of paralytic shellfish toxin-producingmicroalgae using machine learning and deep learning methods

Wei XU; Jie NIU; Wenyu GAN; Siyu GOU; Shuai ZHANG; Han QIU; Tianjiu JIANG

doi:10.1007/s00343-022-1312-1

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Identification of paralytic shellfish toxin-producingmicroalgae using machine learning and deep learning methods

Marine harmful microalgae | Updated：2023-05-20

- Identification of paralytic shellfish toxin-producingmicroalgae using machine learning and deep learning methods
- Journal of Oceanology and Limnology Vol. 40, Issue 6, Pages: 2202-2217(2022)
- Affiliations：
  
  1.Research Center of Red Tides and Marine Biology, Jinan University, Guangzhou 510632, China
  2.Atmospheric Sciences and Global Change Division, Pacific Northwest National Laboratory, Richland 99354, WA, USA
  3.Key Laboratory of Eutrophication and Red Tide Prevention of Harmful Algae and Marine Biology, Jinan University, Guangzhou 510632, China
- Author bio：
  
  Jie NIU, E-mail: jniu@jnu.edu.cn
  Tianjiu JIANG, E-mail: tjiangtj@jnu.edu.cn
- Funds：
- DOI：10.1007/s00343-022-1312-1
  CLC：
- Received：01 October 2021，
  
  Accepted：28 November 2021，
  
  Online First：11 January 2022，
  
  Published：2022-11
- Accepted：
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Wei XU, Jie NIU, Wenyu GAN, et al. Identification of paralytic shellfish toxin-producingmicroalgae using machine learning and deep learning methods[J]. Journal of Oceanology and Limnology, 2022, 40(6): 2202-2217.
DOI：

Wei XU, Jie NIU, Wenyu GAN, et al. Identification of paralytic shellfish toxin-producingmicroalgae using machine learning and deep learning methods[J]. Journal of Oceanology and Limnology, 2022, 40(6): 2202-2217. DOI： 10.1007/s00343-022-1312-1.

摘要

Abstract

Paralytic shellfish poisoning (PSP) microalgae

as one of the harmful algal blooms

causes great damage to the offshore fishery

marine culture

and marine ecological environment. At present

there is no technique for real-time accurate identification of toxic microalgae

by combining three-dimensional fluorescence with machine learning (ML) and deep learning (DL)

we developed methods to classify the PSP and non-PSP microalgae. The average classification accuracies of these two methods for microalgae are above 90%

and the accuracies for discriminating 12 microalgae species in PSP and non-PSP microalgae are above 94%. When the emission wavelength is 650–690 nm

the fluorescence characteristics bands (excitation wavelength) occur differently at 410–480 nm and 500–560 nm for PSP and non-PSP microalgae

respectively. The identification accuracies of ML models (support vector machine (SVM)

and k-nearest neighbor rule (

-NN))

and DL model (convolutional neural network (CNN)) to PSP microalgae are 96.25%

96.36%

and 95.88% respectively

indicating that ML and DL are suitable for the classification of toxic microalgae.

关键词

Keywords

references

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