Cited by 6Marine Toxins and Detection MethodsSpectroscopy and Chemometric AnalysesAquatic Ecosystems and Phytoplankton Dynamics

Early rapid detection of ichthyotoxic algae based on fluorescent quaternion features via neural network

Shan, Shihan, Yi, Kehan, Tong, Mengmeng, Wang, Xiaoping, Cai, Hui·2023·Algal Research

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Crossref · 10.1016/j.algal.2023.103276

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  • Industrial-scale raceway cultivation of Arthrospira platensis at 300 m2 under natural sunlight has demonstrated year-round operation, though seasonal light variation caused up to 65% loss of potential biomass productivity and harvesting inefficiencies a further 30% loss.

    “Industrial-scale raceway cultivation of Arthrospira platensis at 300 m2 under natural sunlight has demonstrated year-round operation, though seasonal light variation caused up to 65% loss of potential biomass productivity and harvesting inefficiencies a further 30% loss [48].”

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Authors
Shan, S., Yi, K., Tong, M., Wang, X., & Cai, H.
Affiliations
Zhejiang University (CN)
Corresponding author
Wang, Xiaoping
Venue
Algal Research
Publisher
Elsevier BV
Published
2023-09
Volume
75
Pages
103276
ISSN
2211-9264 (ISSN-L)
OpenAlex
W4387107315
Cited by
6
References
46
Citations by year
2026: 4 · 2025: 2
FWCI
0.52
Type
journal article
Language
en
Open access
closed
Locations
Algal Research (publishedVersion)
Funders
  • Guangxi Key Research and Development Program (AB22080081)
  • Bureau of Science and Technology of Zhoushan (2019C81031)
Topics
  • Physical Sciences › Environmental Science › Environmental Chemistry › Marine Toxins and Detection Methods
  • Physical Sciences › Chemistry › Analytical Chemistry › Spectroscopy and Chemometric Analyses
  • Physical Sciences › Environmental Science › Environmental Chemistry › Aquatic Ecosystems and Phytoplankton Dynamics
Keywords
harmful algal blooms, fluorescence spectroscopy, excitation and emission spectra, convolutional neural networks, species identification, early detection, Amphidinium carterae, Karenia mikimotoi, Heterosigma akashiwo, Prymnesium parvum, water quality monitoring

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