05 Apr 2023
Florida Atlantic University platform uses digital holographic microscopy in open waters.
A project at Florida Atlantic University (FAU) has developed a laser-based platform designed to detect harmful blooms of Karenia brevis algae in ocean waters, a phenomenon known as red tide.The algae can be a recurring problem in the coastal Gulf of Mexico, creating toxins that cause death in fish, dolphins, manatees and birds, as well as respiratory irritation in humans.
An ability to detect red tide blooms at all life stages and cell concentrations is critical to increasing predictive capabilities and developing potential mitigation strategies, according to the FAU project. But current microscopic techniques are limited in their application and expensive to deploy.
The answer could be AUTOHOLO, a novel autonomous, submersible, 3D holographic microscope and imaging system, designed to study marine particles and plankton in their natural environment.
Reported in Harmful Algae, the study is the first to utilize holography to characterize red tide in the field and monitor harmful algal blooms (HABs), tackling limitations associated with current methods that usually rely on sample collections at fixed locations.
AUTOHOLO builds on previous research into characterizing oceanic particles using optical techniques at the FAU lab of Aditya Nayak, which concluded that holography is currently the only viable non-intrusive technique for fully characterizing oceanic particle distributions over a three-dimensional sample volume.
Put into practice in the AUTOHOLO device, the holographic technique illuminates a sample volume with a coherent and collimated beam of 532-nanometer light, and records the diffraction patterns resulting from interference between light scattered by particles in the volume and the undisturbed light beam. Numerical reconstruction from the holographic diffraction patterns produces in-focus images of all particles within the volume, giving 3D information on their shape, distributions and motion.
The current AUTOHOLO prototype involves two cylindrical tubes, six inches in diameter and 40 inches long. One houses the laser source along with beam expanding and alignment optics, and the other contains a 16MP camera sensor, power supply and control electronics. Data is stored onboard using 4TB solid state hard drives. The sample volume provided by the water-filled tube is "an order of magnitude higher than the volume sampled per hologram in commercially available holographic imagers," according to the designers.
Accurate analysis across large distances
"Our researchers designed the AUTOHOLO to be versatile enough to overcome challenges associated with small or fixed sample volumes as well as environments that are visually complex, to be used as a warning system for red tide," commented FAU's Stella Batalama.
In trials, the AUTOHOLO took field measurements in the coastal Gulf of Mexico during an active K. brevis bloom over the 2020-2021 winter season, using a custom-built towing system designed to help record data over large spatial ranges. Surface and sub-surface water samples were also collected and analyzed in the lab using benchtop holographic imaging and flow cytometry for validation.
A dataset of red tide cells created from the holographic images was used to train a customized convolutional neural network for automated classification.
Results across diverse datasets with red tide concentrations at varying levels showed that the AUTOHOLO delivered 90 percent accuracy in its analysis and confirmed its ability to characterize particle abundance over large spatial distances, potentially facilitating rapid characterization over large areas during bloom events.
"Red tide blooms can occupy varying depths in the water column, and surface focused single point sampling or sampling at limited discrete depths may under-sample or miss any population aggregating at a depth," said Malcolm McFarland from the FAU's Florida Center for Coastal and Human Health.
"In the future, the AUTOHOLO could be integrated into existing HAB monitoring networks to enhance the capability of detecting red tide in aquatic environments around the world."
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