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Microplastic quantification and chemical characterization in salt samples, using stereomicroscopy, smartphone camera and supervised machine learning tools
Summary
Scientists developed a cheap, easy way to spot microplastics in everyday foods using a smartphone camera and computer smarts, rather than expensive lab equipment. Testing sea salt from Mexico, they found nearly 2,000 tiny plastic pieces per kilogram, made of common plastics like polypropylene and PVC, a reminder that the salt we sprinkle on our food may come with an unwanted side of plastic. This tool could make it easier and cheaper for labs everywhere to routinely check our food supply for microplastic contamination.
The microplastic (MPs) pollution, especially in the oceans, has a direct impact on seafood products, particularly sea salt. This is a growing concern due to the high daily consumption of salt and the potential adverse health effects associated with MPs exposure. This study presents a rapid, cost-effective, and green approach for MPs-detection in food samples employing stereomicroscopy, smartphone-cameras, image analysis, and Machine-Learning tools. The method integrates accessible image acquisition with open-source software (Ilastik/FIJI) to enable automated detection and measurement of two common morphologies: filaments and fragments, providing a straightforward alternative for laboratories with limited resources. The developed models demonstrated high sensitivity (100% filaments, 76.5% fragments) and acceptable specificity (76.4% filaments, 72.2% fragments), with an average recovery of 108.6% for spiked samples. Upon application to sea salt samples from México, the model identified 1950 ± 354 MPs pieces/kg of salt. Among the detected MPs, filaments were the predominant morphology (1235 ± 263 MPs pieces/kg), while fragment count was 715 ± 167 MPs pieces/kg. Polypropylene, polyethylene, Polyvinyl chloride, and cellophane were confirmed via FTIR analysis. The method was successfully applied to 6 additional food matrices (e.g., sugar, meat tenderizer). The proposed methodology is delimited to the detection of microplastics ≥300 μm (filaments) and ≥200 μm (fragments), and therefore does not account for smaller particles. The proposed method offers a rapid, replicable and accessible alternative that enables high-throughput preliminary microplastic detection while reserving chemical identification for complementary spectroscopic analysis, thus suggesting a potential for routine application in microplastic monitoring.