In situ digital holographic microscopy for rapid detection and monitoring of the harmful dinoflagellate, Karenia brevis

Ranjoy Barua, Delaney Sanborn, Lisa Nyman, Malcolm McFarland, Timothy Moore, Jiarong Hong, Matt Garrett, Aditya R. Nayak

Research output: Contribution to journalArticlepeer-review

Abstract

Karenia brevis blooms, also known as red tide, are a recurring problem in the coastal Gulf of Mexico. These blooms have the capacity to inflict substantial damage to human and animal health as well as local economies. Thus, monitoring and detection of K. brevis blooms at all life stages and cell concentrations is essential for ensuring public safety. Current K. brevis monitoring methods have several limitations, including size resolution limits and concentration ranges, limited capacity for spatial and temporal profiling, and/or small sample volume processing. Here, a novel monitoring method wherein an autonomous digital holographic imaging microscope (AUTOHOLO), that overcomes these limitations and can characterize K. brevis concentrations in situ, is presented. Using the AUTOHOLO, in situ field measurements were conducted in the coastal Gulf of Mexico during an active K. brevis bloom over the 2020–21 winter season. Surface and sub-surface water samples collected during these field studies were also analyzed in the lab using benchtop holographic imaging and flow cytometry for validation. A convolutional neural network was trained for automated classification of K. brevis at all concentration ranges. The network was validated with manual counts and flow cytometry, yielding a 90% accuracy across diverse datasets with varying K. brevis concentrations. The usefulness of pairing the AUTOHOLO with a towing system was also demonstrated for characterizing particle abundance over large spatial distances, which could potentially facilitate characterization of spatial distributions of K. brevis during bloom events. Future applications of the AUTOHOLO can include integration into existing HAB monitoring networks to enhance detection capabilities for K. brevis in aquatic environments around the world.

Original languageEnglish (US)
Article number102401
JournalHarmful Algae
Volume123
DOIs
StatePublished - Mar 2023

Bibliographical note

Funding Information:
This study, including the entire field effort and the development of the towing system was funded by the Florida Fish and Wildlife Conservation Commission . The AUTOHOLO was developed using funds from the National Science Foundation Ocean Technology and Interdisciplinary Coordination (OTIC) program (Award # 1634053 ). Additionally, ARN was supported through a National Academy of Sciences, Engineering, and Medicine (NASEM) Gulf Research Program (GRP) Early Career Research Fellowship. RB was also partially supported by an FAU Ocean and Mechanical Engineering departmental assistantship. The machine learning algorithm development was partially supported through an award from the Great Lakes Observing System (GLOS). The authors are also grateful to Csaba Vaczo at FAU for the towing design, Jason Law and Chad Lembke at University of Southern Florida for field support, Stephanie Schreiber at FAU for flow cytometry support, and Kate Hubbard of FFWCC for useful discussions and comments.

Funding Information:
This study, including the entire field effort and the development of the towing system was funded by the Florida Fish and Wildlife Conservation Commission. The AUTOHOLO was developed using funds from the National Science Foundation Ocean Technology and Interdisciplinary Coordination (OTIC) program (Award #1634053). Additionally, ARN was supported through a National Academy of Sciences, Engineering, and Medicine (NASEM) Gulf Research Program (GRP) Early Career Research Fellowship. RB was also partially supported by an FAU Ocean and Mechanical Engineering departmental assistantship. The machine learning algorithm development was partially supported through an award from the Great Lakes Observing System (GLOS). The authors are also grateful to Csaba Vaczo at FAU for the towing design, Jason Law and Chad Lembke at University of Southern Florida for field support, Stephanie Schreiber at FAU for flow cytometry support, and Kate Hubbard of FFWCC for useful discussions and comments.

Publisher Copyright:
© 2023 Elsevier B.V.

Keywords

  • Convolutional neural network
  • Gulf of Mexico
  • Harmful algal bloom
  • Holography
  • In situ imaging
  • Karenia brevis
  • Plankton monitoring
  • Red tide

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