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Laser induced fluorescence and machine learning: a novel approach to microplastic identification
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Researchers combined laser-induced fluorescence with machine learning to identify microplastics in water in real time, achieving 97.6% accuracy in distinguishing plastics from other organic matter and 88.3% accuracy in identifying specific plastic types — a promising step toward automated ocean monitoring.
Identifying the types of materials such as plastics, microplastics, and oil pollutants is essential for understanding their effects on marine life. We propose a new methodology for the real-time detection and identification of microplastics in aquatic environments. Our experiments are based on a compact Laser Induced Fluorescence (LIF) device, with machine learning techniques applied to classify the materials. A 405 nm CW laser excitation source effectively induces fluorescence spectra in the visible spectrum from material samples that are either floating or submerged in water. We examine known plastic pollutants in seawater, including polyethylene (PE), polypropylene (PP), polystyrene (PS) and polyethylene terephthalate (PET), as well as maritime fuels, lubricating oils, and other organic substances that are abundant in the marine environment. Our two-step identification process first employs machine learning algorithms to distinguish microplastics from other organic materials with a high degree of accuracy (97.6%). Subsequently, the type of plastic is determined with an accuracy of 88.3% in a second application of machine learning techniques.
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Application of Artificial Intelligence to Improve Microplastic Identification
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This review examines how artificial intelligence -- including machine learning and computer vision approaches -- can improve the identification, classification, and measurement of microplastics during environmental sampling and analysis. AI-assisted identification addresses a major bottleneck in microplastic research by reducing the time and expertise required to process large sample volumes, enabling faster and more consistent data generation needed to assess human health exposure at scale.
Role of Machine Learning and Machine Vision on Microplastics
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Scientists are teaching computers to spot and identify microplastics in samples of water, soil, and food much faster than humans can by eye, this review paper explains how these AI tools work and where they still fall short. Faster, more accurate detection matters because it could help researchers track how much plastic contamination is getting into our environment and food supply, which is an important step toward understanding potential health risks. The technology still needs better cameras, shared databases, and standardized methods before it can be reliably used everywhere.
Defining Irregular Microplastics: A Machine Learning Approach for Morphometric Characterization
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Researchers applied a machine learning decision-tree classifier to hyperspectral images of 129 nano- and microplastic particles and identified five morphometric descriptors—circularity, roundness, solidity, long diameter, and perimeter-area ratio—that together distinguish irregular particles from spherical and fibrous forms with 96% accuracy, providing a standardized definition for the most environmentally prevalent but least-defined shape class.
Advanced Microplastic Identification in Marine Environments via Hybrid Deep Learning
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Researchers propose a hybrid deep learning architecture combining 3D convolutional neural networks and Vision Transformers applied to hyperspectral imagery to detect and classify microplastics in turbid marine environments, capturing both local spectral signatures and global contextual patterns that single-model approaches miss.
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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.