HealthTech Research

Machine learning scopes out previously “invisible” microplastics

Machine learning scopes out previously “invisible” microplastics

A new approach combines 3D coherent imaging with to detect microscale microplastics in filtered water samples.

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SwissCognitiveThe largest percentage of marine litter consists of plastic waste, in which the biodegradation process can take decades to complete. With serious concerns related to the negative effects of plastic, especially microplastics, on fragile marine environments, a means of identifying, isolating, and extracting these environmental hazards is sorely needed.

The term “microplastics” refers to plastic material with a diameter smaller than 5 mm, commonly generated from the breakup of larger items or otherwise mass produced to match industrial and market needs — think microbeads as exfoliating agents in cosmetics and soaps. What is perhaps even more alarming is the fact that they are found in high abundance at the sea surface, in the water column, and on the seabed, which includes the deep sea. Recent studies have also detected microplastics in freshwater and drinking water sources, causing great concern about their potential threat to human health.

Typical procedures for microplastic identification in environmental samples usually rely on visual sorting by expert users under an optical microscope, although this is only possible when the particle size ranges between 1 and 5 mm. User error notwithstanding, anything smaller than this range goes unaccounted for. It therefore goes without saying that an automated and robust identification and counting method is badly needed to perform effective ecological risk assessments. Complexity of the microplastic identification problem Now, researchers from the Institute of Applied Sciences and Intelligent Systems (ISASI) and the National Research Council (CNR) of Italy aim to solve this problem using a newly developed approach, termed “holographic plastics identification” (HPI), which c ombines 3D coherent imaging with to detect microscale microplastics in filtered water samples. Their study was recently published in Advanced Intelligent Systems .

In holography, photographic recordings are captured using the interference pattern between two or more beams of light. The holograms that are generated as a result can appear three-dimensional. In the past, digital holography has been used to image microplastic particles, providing a fast and effective means of identification with the potential to provide a low-cost, field-portable system for real-time environmental monitoring. The team plans to take it one step further by combining the technique with to improve its accuracy and capability.

“We used a paradigm relying on features extracted from holographic images,” said the authors of the study. “We demonstrate that it is possible to determine an optimal set of ‘holographic features’ extracted from the digital holograms, with the scope of identifying a distinctive marker for the [microplastic] class. Thus, these can be thought of as a specific ‘fingerprint’ for the whole MP population.”

Real-time, automatic recognition of microplastics in marine water samples is an ambitious goal given that they contain a number of other components, including microscale organisms such as plankton and nekton, that can be easily confused with microplastics. To circumvent this problem, the researchers created a library of holographic images for ten populations of micro‐objects: nine diatom species — a type of single-celled plankton — and a heterogeneous mix of microplastics between 20 μm and 1 mm, which includes polystyrene, polyethylene, polypropylene, polyvinylchloride (PVC), and PE terephthalate.

“After recording digital holograms of the samples, we applied holographic object detection and automatic refocusing to reconstruct the complex wavefronts of each object in the field of view. Then, we extract the modulus‐2π or ‘wrapped’ phase images, from which we segment the detected objects … From the acquired sequences of reconstructed holograms, we identified a total of 2000 objects evenly distributed among the ten classes.” […]


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