AI-Enabled Microplastic Detection in Food and Water Samples: A Fluorescence–Microscopy Integrated Approach for Sustainable Food Security

Deep Learning Approaches for Microplastic Identification in Microscopic Water Samples

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.

Detection and Identification of Microplastics in Food and the Environment

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Researchers outlined methods for detecting and identifying microplastics across a range of environmental and food samples, emphasizing that different matrices — such as soil, water, air, and food — require tailored analytical approaches to accurately measure contamination levels.

Microplastics: a cancer-causing agent for humans and prospects for identification using AI and modern approaches

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Tiny plastic particles called microplastics, smaller than a grain of rice, some invisible to the eye, can get into your body through the air you breathe, the food you eat, and even your skin, where they may trigger harmful biological reactions linked to cancer risk. This review paper pulls together existing research on how these particles behave in the body and highlights promising AI-based tools that could help scientists detect and study microplastics more effectively. The takeaway: microplastics are a growing health concern worth watching, and better detection methods are key to understanding the real risks they pose.

Quantitative Detection of Microplastics in Water Through Real-Time Analysis of Fluorescence Signal

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Researchers developed a real-time fluorescence-based detection method for quantifying microplastics in water, addressing limitations of existing techniques and enabling faster, more sensitive monitoring of microplastic concentrations in environmental samples.