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

  • Marine Harmful Microalgae
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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 410–180 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 (k-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.

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  1. Wei XU and Jie NIU contributed equally to this work and should be regarded as co-first authors.

Authors and Affiliations

  1. Research Center of Red Tides and Marine Biology, Jinan University, Guangzhou, 510632, China

    Wei Xu, Jie Niu, Wenyu Gan, Siyu Gou, Shuai Zhang & Tianjiu Jiang

  2. Atmospheric Sciences and Global Change Division, Pacific Northwest National Laboratory, Richland, WA, 99354, USA

    Han Qiu

  3. Key Laboratory of Eutrophication and Red Tide Prevention of Harmful Algae and Marine Biology, Jinan University, Guangzhou, 510632, China

    Tianjiu Jiang

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  1. Wei Xu
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Correspondence to Jie Niu or Tianjiu Jiang.

Additional information

Supported by the National Natural Science Foundation of China (No. 41972244), partially supported by the Science and Technology Basic Resources Survey of the Ministry of Science and Technology (No. 2018FY100201), and the National Key Research and Development Program (No. 2019YFC1407900) to Siyu GOU, Shuai ZHANG, Wenyu GAN, and Tianjiu JIANG

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Xu, W., Niu, J., Gan, W. et al. Identification of paralytic shellfish toxin-producing microalgae using machine learning and deep learning methods. J. Ocean. Limnol. 40, 2202–2217 (2022). https://doi.org/10.1007/s00343-022-1312-1

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