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A Review on Microplastics: The Emerging Threat to Food Safety
Summary
This review pulls together existing research showing that tiny plastic particles called microplastics are widely found in everyday foods, especially salt and seafood, and can build up in our bodies after we eat them. Scientists have linked this accumulation to potential health problems like brain issues, liver and kidney damage, and fertility troubles, though researchers still need better standardized tools to detect microplastics and stronger strategies to reduce our exposure to them.
Microplastics (MPs), defined as plastic particles less than 5 mm (i.e., 5000 µm) in diameter, have emerged as pervasive environmental pollutants owing to the massive global production of plastics and their widespread use. Significant research has been conducted worldwide in the last few years to evaluate the severity of MP pollution in the environment, assess the human health hazards caused by MPs, and establish novel detection techniques. However, there are very few review articles available that provide a comprehensive overview of this new-age food matrix pollutant. This review provides an overview of the severity of MP contamination in food, emphasizing the types of MPs, their possible routes of transmission, and possible disease mechanisms. This review also focuses on advancements in MP detection techniques in food matrices, with a particular focus on AI-assisted methods, regulatory measures/policies adopted by different countries, and recent research undertaken to mitigate MPs from the environment and food. The data presented here are based on the results of a thorough literature search, which was conducted across multiple research databases for the period from to 2013-2025. The search results revealed the presence of high levels of MPs in commercial food products, particularly salt and seafood, with common polymers such as polyethylene (PE) and polypropylene (PP). The accumulation of MPs in the human body due to ingestion could be linked to serious health effects, such as neurological dysfunction, liver fibrosis, kidney damage, and impaired reproductive function. AI-assisted computed tomography (CT) imaging using the DeepLabV3+ semantic segmentation model has demonstrated highly promising results, achieving detection accuracies of 99–100% in fish tissue samples. Despite these advancements in MP research, critical challenges remain in the standardization of detection techniques and the establishment of effective mitigation strategies.