Cite this paper:
Wei XU, Jie NIU, Wenyu GAN, Siyu GOU, Shuai ZHANG, Han QIU, Tianjiu JIANG. Identification of paralytic shellfish toxin-producingmicroalgae using machine learning and deep learning methods[J]. Journal of Oceanology and Limnology, 2022, 40(6): 2202-2217

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

Wei XU1, Jie NIU1, Wenyu GAN1, Siyu GOU1, Shuai ZHANG1, Han QIU2, Tianjiu JIANG1,3
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
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 (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.
Key words:    paralytic shellfish poisoning (PSP)|machine learning (ML)|deep learning (DL)|toxic algal classification   
Received: 2021-10-01   Revised:
PDF (2749 KB) Free
Print this page
Add to favorites
Email this article to others
Articles by Wei XU
Articles by Jie NIU
Articles by Wenyu GAN
Articles by Siyu GOU
Articles by Shuai ZHANG
Articles by Han QIU
Articles by Tianjiu JIANG
Aas K, Eikvil L. 1999. Text Categorization: A Survey.Norwegian Computing Center.
Alexander R, Gikuma-Njuru P, Imberger J. 2012. Identifying spatial structure in phytoplankton communities using multi-wavelength fluorescence spectral data and principal component analysis. Limnology and Oceanography:Methods, 10(6): 402-415,
Alimjan G, Sun T L, Liang Y et al. 2018. A new technique for remote sensing image classification based on combinatorial algorithm of SVM and KNN. International Journal of Pattern Recognition and Artificial Intelligence, 32(7): 1859012,
Alizadeh J M, Kavianpour M R, Danesh M et al. 2018. Effect of river flow on the quality of estuarine and coastal waters using machine learning models. Engineering Applications of Computational Fluid Mechanics, 12(1): 810-823,
Anderson D M, Cembella A D, Hallegraeff G M. 2012a.Progress in understanding harmful algal blooms:paradigm shifts and new technologies for research, monitoring, and management. Annual Review of Marine Science, 4: 143-176,
Anderson D M, Alpermann T J, Cembella A D et al. 2012b.The globally distributed genus Alexandrium: multifaceted roles in marine ecosystems and impacts on human health.Harmful Algae, 14: 10-35,
Andrinolo D, Michea L F, Lagos N. 1999. Toxic effects, pharmacokinetics and clearance of saxitoxin, a component of paralytic shellfish poison (PSP), in cats. Toxicon, 37(3):447-464,
Bahram M, Bro R, Stedmon C et al. 2006. Handling of Rayleigh and Raman scatter for PARAFAC modeling of fluorescence data using interpolation. Journal of Chemometrics, 20(3-4): 99-105,
Beutler M, Wiltshire K H, Meyer B et al. 2002. A fluorometric method for the differentiation of algal populations in vivo and in situ. Photosynthesis Research, 72(1): 39-53,
Bidigare R R, Ondrusek M E, Morrow J H et al. 1990. In-vivo absorption properties of algal pigments. In: Proceedings of SPIE 1302, Ocean Optics X. SPIE, Orlando, USA.p.290-302,
Boser B E, Guyon I M, Vapnik V N. 1992. A training algorithm for optimal margin classifiers. In: Proceedings of the 5th Annual Workshop on Computational Learning Theory. ACM, Pittsburgh, USA. p.144-152,
Buhmann M D. 2000. Radial basis functions. Acta Numerica, 9: 1-38.
Cai Q S, Li R X, Zhen Y et al. 2006. Detection of two Prorocentrum species using sandwich hybridization integrated with nuclease protection assay. Harmful Algae, 5(3): 300-309,
Cao J R, Huan Q L, Wu N et al. 2015. Effects of temperature, light intensity and nutrient condition on the growth and hemolytic activity of six species of typical ichthyotoxic algae. Marine Environmental Science, 34(3): 321-329. (in Chinese with English abstract)
Cherkassky V. 1997. The nature of statistical learning theory.IEEE Transactions on Neural Networks, 8(6): 1564,
Cortes C, Vapnik V N. 1995. Support-vector networks. Machine Learning, 20(3): 273-297,
Cover T M. 1968. Estimation by the nearest neighbor rule.IEEE Transactions on Information Theory, 14(1): 50-55,
Cusick K D, Sayler G S. 2013. An overview on the marine neurotoxin, saxitoxin: genetics, molecular targets, methods of detection and ecological functions. Marine Drugs, 11(4): 991-1018,
Divya O, Mishra A K. 2007. Multivariate methods on the excitation emission matrix fluorescence spectroscopic data of diesel-kerosene mixtures: a comparative study.Analytica Chimica Acta, 592(1): 82-90,
Duan Y L, Su R G, Shi X Y et al. 2012. Differentiation of phytoplankton populations by in vivo fluorescence based on high-frequency component of wavelet. Chinese Journal of Lasers, 39(7): 0715003. (in Chinese with English abstract)
Guillard R R L, Ryther J H. 1962. Studies of marine planktonic diatoms: I. Cyclotella nana hustedt, and Detonula confervacea (cleve) gran. Canadian Journal of Microbiology, 8(2): 229-239,
Hallegraeff G M. 1993. A review of harmful algal blooms and their apparent global increase. Phycologia, 32(2): 79-99,
Han J, Park J S, Park Y et al. 2021. Effects of paralytic shellfish poisoning toxin-producing dinoflagellate Gymnodinium catenatum on the marine copepod Tigriopus japonicus.Marine Pollution Bulletin, 163: 111937,
Hingane M C, Matkar S B, Mane A B et al. 2015.Classification of MRI brain image using SVM classifier.IJSTE -International Journal of Science Technology & Engineering, 1(9): 24-28.
Huan Q, Huang X, Wu N et al. 2013. Identification of Ichthyotoxic Microalgae Species and Its Hemolytic Activity by Three-Dimensional Fluorescence Spectra.Spectroscopy and Spectral Analysis, 33(2): 399-403. (in Chinese with English abstract)
Ignatiades L, Gotsis-Skretas O. 2010. A review on toxic and harmful algae in Greek Coastal Waters (E. Mediterranean Sea). Toxins, 2(5): 1019-1037,
Jaeckisch N, Yang I, Wohlrab S et al. 2011. Comparative genomic and transcriptomic characterization of the toxigenic marine dinoflagellate Alexandrium ostenfeldii.PLoS One, 6(12): e28012,
Jeffrey S W, Hallegraeff G M. 1980. Studies of phytoplankton species and photosynthetic pigments in a warm core eddy of the East Australian Current. I. Summer populations.Marine Ecology Progress Series, 3: 285-294,
Jiang T, Wang R, Wu N et al. 2011. Study on hemolytic activity of Chattonella marina Hong Kong strain. Environmental Science, 32(10): 2920-2925. (in Chinese with English abstract)
Johnsen G, Samset O, Granskog L et al. 1994. In vivo absorption characteristics in 10 classes of bloom-forming phytoplankton: taxonomic characteristics and responses to photoadaptation by means of discriminant and HPLC analysis. Marine Ecology Progress Series, 105: 149-157,
Kellmann R, Mihali T K, Jeon Y J et al. 2008. Biosynthetic intermediate analysis and functional homology reveal a saxitoxin gene cluster in cyanobacteria. Applied and Environmental Microbiology, 74(13): 4044-4053,
Kotaki Y, Tajiri M, Oshima Y et al. 1983. Identification of a calcareous red alga as the primary source of paralytic shellfish toxins in coral reef crabs and gastropods. Bulletin of the Japanese Society of Scientific Fisheries, 49(2): 283-286,
Kumar M S, Sharma S A. 2021. Toxicological effects of marine seaweeds: a cautious insight for human consumption.Critical Reviews in Food Science and Nutrition, 61(3):500-521, 4.
LeCun Y. 1992. A theoretical framework for back-propagation.In: Mehra P, Wah B eds. Artificial Neural Networks:Concepts and Theory. IEEE, Los Alamitos.
LeCun Y, Bengio Y, Hinton G. 2015. Deep learning. Nature, 521(7553): 436-444,
LeCun Y, Boser B, Denker J S et al. 1989a. Backpropagation applied to handwritten zip code recognition. Neural Computation, 1(4): 541-551.
LeCun Y, Boser B, Denker J S et al. 1989b. Handwritten digit recognition with a back-propagation network. In:Proceedings of the 2nd International Conference on Neural Information Processing Systems. Morgan Kaufmann, Denver, USA. p.396-404.
LeCun Y, Bottou L, Bengio Y et al. 1998. Gradientbased learning applied to document recognition.Proceedings of the IEEE, 86(11): 2278-2324,
Lee T Y, Tsuzuki M, Takeuchi T et al. 1995. Quantitative determination of cyanobacteria in mixed phytoplankton assemblages by an in vivo fluorimetric method.Analytica Chimica Acta, 302(1): 81-87,
Louchard E M. Reid R P, Stephens C F et al. 2002. Derivative analysis of absorption features in hyperspectral remote sensing data of carbonate sediments. Optics Express, 10(26): 1573-1584,
Lu L. 2007. Study on Fluorescence Spectra for Identifying Phytoplankton Community. Ocean University of China, Qingdao, China. (in Chinese with English abstract)
Lü G C, Zhao W H, Wang J T. 2011. Applications of threedimensional fluorescence spectrum of dissolved organic matter to identification of red tide algae. Spectroscopy and Spectral Analysis, 31(1): 141-144. (in Chinese with English abstract)
Ma Y M, Gao J Y, Wang Q H. 2007. Forecast model for red tide on artificial neural network. Marine Forecasts, 24(1):38-44. (in Chinese with English abstract)
Masó M, Garcés E. 2006. Harmful microalgae blooms (HAB);problematic and conditions that induce them. Marine Pollution Bulletin, 53(10-12): 620-630,
Merry R J E. 2005. Wavelet Theory and Applications: A Literature Study. Eindhoven University of Technology Department of Mechanical Engineering Control Systems Technology Group.
Millie D F, Schofield O M, Kirkpatrick G J et al. 1997.Detection of harmful algal blooms using photopigments and absorption signatures: a case study of the Florida red tide dinoflagellate, Gymnodinium breve. Limnology and Oceanography, 42(5): 1240-1251,
Moberg L, Karlberg B, Sørensen K et al. 2002. Assessment of phytoplankton class abundance using absorption spectra and chemometrics. Talanta, 56(1): 153-160,
Mosavi A, Salimi M, Faizollahzadeh Ardabili S et al. 2019.State of the art of machine learning models in energy systems, a systematic review. Energies, 12(7): 1301,
O’Neil J M, Davis T W, Burford M A et al. 2012. The rise of harmful cyanobacteria blooms: the potential roles of eutrophication and climate change. Harmful Algae, 14:313-334,
Ozawa T, Ishihara S, Fujishiro M et al. 2020. Automated endoscopic detection and classification of colorectal polyps using convolutional neural networks. Therapeutic Advances in Gastroenterology, 13: 175628482091065,
Paerl H W, Gardner W S, Havens K E et al. 2016. Mitigating cyanobacterial harmful algal blooms in aquatic ecosystems impacted by climate change and anthropogenic nutrients.Harmful Algae, 54: 213-222,
Poryvkina L, Babichenko S, Leeben A. 2000. Analysis of phytoplankton pigments by excitation spectra of fluorescence. In: Proceedings of EARSeL-SIG-Workshop LIDAR. FRG, Dresden, Germany. p.224-232.
Qi X L, Wu Z Z, Zhang C S et al. 2016. A fluorescence technology for discriminating toxic algae by support sector machine regression. Periodical of Ocean University of China, 46(12): 73-80. (in Chinese with English abstract)
Raphael A, Dubinsky Z, Iluz D et al. 2020. Deep neural network recognition of shallow water corals in the Gulf of Eilat (Aqaba). Scientific Reports, 10(1): 12959,
Rasmussen S A, Andersen A J C, Andersen N G et al. 2016.Chemical diversity, origin, and analysis of phycotoxins.Journal of Natural Products, 79(3): 662-673,
Sebastiani F. 2002. Machine learning in automated text categorization. ACM Computing Surveys, 34(1): 1-47.Seppälä J, Olli K. 2008. Multivariate analysis of phytoplankton spectral in vivo fluorescence: estimation of phytoplankton biomass during a mesocosm study in the Baltic Sea.Marine Ecology Progress Series, 370: 69-85,
Sommer H, Monnier R P, Riegel B et al. 1948. Paralytic shellfish poison. I. Occurrence and concentration by ion exchange. Journal of the American Chemical Society, 70(3): 1015-1018,
Sommer H, Whedonc W F, Kofoid A et al. 1937. Relation of paralytic shellfish poison to certain plankton organisms of the genus Gonyaulax. Archives of Pathology, 24(5): 537-559.
Sultana F, Sufian A, Dutta P. 2019. Advancements in image classification using convolutional neural network. IEEE,
Tang X H, Yu R C. Zhou M J et al. 2012. Application of rRNA probes and fluorescence in situ hybridization for rapid detection of the toxic dinoflagellate Alexandrium minutum. Chinese Journal of Oceanology and Limnology, 30(2): 256-263,
Taroncher-Oldenburg G, Kulis D M, Anderson D M. 1997.Toxin variability during the cell cycle of the dinoflagellate Alexandrium fundyense. Limnology and Oceanography, 42(5): 1178-1188,
Taroncher-Oldenburg G, Kulis D M, Anderson D M. 1999. Coupling of saxitoxin biosynthesis to the G1 phase of the cell cycle in the dinoflagellate Alexandrin fundyense: temperature and nutrient effects. Natural Toxins, 7(5): 207-219,<207::AID-NT61>3.0.CO;2-Q.
Van Dolah F M, Roelke D, Greene R M. 2001. Health and ecological impacts of harmful algal blooms:risk assessment needs. Human and Ecological Risk Assessment: An International Journal, 7(5): 1329-1345,
Vishwanathan S V M, Narasimha Murty M. 2002. SSVM:a simple SVM algorithm. In: Proceedings of 2002 International Joint Conference on Neural Networks. IEEE, Honolulu, USA. p.2393-2398.
Wang L, Xu X, Dong H et al. 2018. Multi-pixel simultaneous classification of PolSAR image using convolutional neural networks. Sensors (Basel), 18(3): 769,
Wang Q, Pang W J, Mao Y D et al. 2020. Changes of extracellular polymeric substance (EPS) during Microcystis aeruginosa blooms at different levels of nutrients in a eutrophic microcosmic simulation device.Polish Journal of Environmental Studies, 29(1): 349-360,
Xu C M, Jackson S A. 2019. Machine learning and complex biological data. Genome Biology, 20(1): 76,
Yang I, John U, Beszteri S. 2010. Comparative gene expression in toxic versus non-toxic strains of the marine dinoflagellate Alexandrium minutum. BMC Genomics, 11: 248.
Yang P, Li X L. 1998. Study on marine algal toxic food poisoning (review). Chinese Journal of Food Hygiene, 10(1): 40-43, 45. (in Chinese)
Zavala-Mondragon L A, Lamichhane B, Zhang L et al. 2020.CNN-SkelPose: a CNN-based skeleton estimation algorithm for clinical applications. Journal of Ambient Intelligence and Humanized Computing, 11(6): 2369-2380,
Zepp R G, Sheldon W M, Moran M A. 2004. Dissolved organic fluorophores in southeastern US coastal waters:correction method for eliminating Rayleigh and Raman scattering peaks in excitation-emission matrices. Marine Chemistry, 89(1-4): 15-36,
Zhang F, Su R, Wang X Z et al. 2008. Fluorescence Characteristics Extraction and Differentiation of Phytoplankton. Chinese Journal of Lasers, 35(12). (in Chinese with English abstract)
Zhang J, Qiu H, Li X Y et al. 2018a. Real-time nowcasting of microbiological water quality at recreational beaches: a wavelet and artificial neural network-based hybrid modeling approach. Environmental Science & Technology, 52(15): 8446-8455,
Zhang S F, Zhang Y, Lin L et al. 2018b. iTRAQ-Based quantitative proteomic analysis of a toxigenic dinoflagellate Alexandrium catenella at different stages of toxin biosynthesis during the cell cycle. Marine Drugs, 16(12): 491,
Zhuang J X, Cai J B, Wang R X et al. 2020. Deep kNN for medical image classification. In: Proceedings of the 23rd International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, Lima, Peru. p.127-136,
Copyright © Haiyang Xuebao