High-throughput microplastics identification by interpretable machine learning and multi-source spectral integration

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.

A Novel Approach for Fast Microplastic Quantification in Sediments Using Machine Learning—Spectrometer Combinations

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Researchers combined visible near-infrared and FTIR spectroscopy with machine learning models (SVR, PLSR, BPNN) to rapidly estimate concentrations of PE, PP, PS, and PVC microplastics in river and loess sediments without extraction or manual counting. Faster, non-destructive quantification methods are crucial for scaling up microplastic monitoring across diverse environmental matrices and enabling consistent cross-study comparisons of pollution levels.

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.

Deep Learning Approaches for Microplastic Identification in Microscopic Water Samples