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Online detection of floating microplastics in liquids
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This paper presents a novel optical detection model for identifying floating microplastic particles in liquids, using binary signal discrimination to distinguish particle signals from noise. The approach offers a new method for online real-time monitoring of microplastic contamination in water.
Due to the complexity and the diversity of microplastics, several methods can be found in the literature for the detection of these particles. This paper presents a novel detection model for microplastics particles based on binary discrimination between two signals: noisy signal and particle signal over noise. The problem is solved in probabilistic terms under recording corrupted by normal noisy. The model is tested over several synthetic signals, and the performances are evaluated varying both the signal-noise ratio and the dataset balance distribution (the a priori probability of measuring the particle signal P 1 within the set of recorded signal segments). The model can be easily implemented into a microcontroller forming an embedded system for real-time fluid monitoring.
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Researchers reviewed non-invasive optical and imaging technologies for detecting and characterizing floating particles including microplastics in liquids, motivated by growing concern over microplastic contamination in drinking water and food products. They found that advances in computational imaging and spectroscopic methods offer promising pathways for scalable, real-time monitoring of large water volumes.
Optical System for In-situ Detection of Microplastics
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Researchers developed a portable optical system capable of detecting, identifying, continuously monitoring, and quantifying microplastics in situ at natural water bodies. The system uses optical techniques to observe the temporal behavior of microplastic concentrations at fixed locations, enabling real-time environmental monitoring without sample collection and laboratory processing.
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Researchers reviewed the current landscape of optical detection methods for microplastics—spanning Raman and FTIR spectroscopy, SERS, FLIM, hyperspectral imaging, and AI-enhanced analysis—highlighting that while handheld and smartphone-based platforms show strong promise for field deployment, the absence of standardized protocols and interference from organic matter remain key barriers to reliable environmental monitoring.
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Researchers studied the optical properties of micro- and nanoplastics and evaluated near-infrared spectroscopy as a detection method for plastic particles in water, finding that optical techniques show promise for rapid, non-destructive identification. Improved optical detection methods could enable faster and more cost-effective monitoring of plastic pollution in aquatic environments.
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This study examines the potential for optical methods to detect microplastics in ocean water at large spatial scales, noting that while optical detection is promising for overcoming the limitations of discrete water sampling, methods remain in early development and reference libraries of microplastic optical properties are sparse.
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When a large batch of papers lands in the Atlas, we read through it and send a short write-up of what stood out.