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Microplastic quantification and chemical characterization in salt samples, using stereomicroscopy, smartphone camera and supervised machine learning tools
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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.
More Papers Like This
Research data for Microplastic quantification and chemical characterization in salt samples, using stereomicroscopy, smartphone camera and supervised machine learning tools.
AI summary Read the abstract
Scientists developed a new, low-cost method to detect and count tiny plastic bits (microplastics) hiding in salt using a smartphone camera, a basic microscope, and smart computer software instead of expensive lab equipment. This matters because salt is something almost everyone eats daily, and having a cheaper, more accessible way to check it for plastic contamination could help researchers and food safety agencies track this potential health concern more widely. Note that this particular resource is the underlying dataset and code shared for other scientists to verify and reuse the method, rather than a report of new findings about how much microplastic is actually in salt.
Deep-learning enabled rapid and low-cost detection of microplastics in consumer products following on-site extraction and image processing
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Researchers developed a low-cost method to detect microplastics in everyday products like salt, sugar, tea bags, and toothpaste using a smartphone microscope attachment and artificial intelligence. The deep learning system achieved over 90% accuracy in identifying microplastic particles. This affordable approach could help consumers and regulators quickly screen products for microplastic contamination.
Detection of microplastics in sea salt using hyperspectral imaging and machine learning methods: Pollution control in the Mediterranean sea as a case study
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Hyperspectral imaging combined with machine learning was used to detect and classify microplastics in Mediterranean sea salt samples, demonstrating a rapid, non-destructive analytical approach with potential for routine quality control in the food industry.
Detection of Microplastic in Sun-Dried Salt using Spectroscopic Technique
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Researchers used spectroscopic techniques to detect microplastics in sun-dried sea salt from South Korea. Microplastics were found in the salt samples, confirming that sea salt produced from ocean water is a pathway through which humans can ingest plastic particles through everyday food consumption.
Detection and Characterization of Microplastics in Commercial Salts in India
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Researchers detected and characterized microplastics in commercial salts available in India, including rock salt, sea salt, and table salt varieties. The study found widespread microplastic contamination across salt types, raising food safety concerns given the ubiquity of salt as a dietary staple.
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