In situ digital holographic microscopy for rapid detection and monitoring of the harmful dinoflagellate, Karenia brevis.
Convolutional neural network
Gulf of Mexico
Harmful algal bloom
Holography
In situ imaging
Karenia brevis
Plankton monitoring
Red tide
Journal
Harmful algae
ISSN: 1878-1470
Titre abrégé: Harmful Algae
Pays: Netherlands
ID NLM: 101128968
Informations de publication
Date de publication:
03 2023
03 2023
Historique:
received:
12
08
2022
revised:
09
12
2022
accepted:
03
02
2023
entrez:
9
3
2023
pubmed:
10
3
2023
medline:
14
3
2023
Statut:
ppublish
Résumé
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.
Identifiants
pubmed: 36894209
pii: S1568-9883(23)00028-8
doi: 10.1016/j.hal.2023.102401
pii:
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Research Support, U.S. Gov't, Non-P.H.S.
Langues
eng
Sous-ensembles de citation
IM
Pagination
102401Informations de copyright
Copyright © 2023 Elsevier B.V. All rights reserved.
Déclaration de conflit d'intérêts
Declaration of Competing Interest The authors declare no conflict of interest. The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.