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Detection of Microplastics and Heavy Metals Using Electronic Tongues and Machine Learning
Summary
Scientists have developed a smart sensor system—paired with computer learning—that can accurately detect harmful microplastics and heavy metals like zinc and cadmium in seawater, catching contamination over 90% of the time in tests. This matters because these pollutants can build up in the food chain and end up in the seafood and water we consume, so having a faster, reliable way to spot them could help protect drinking water and food supplies before they reach us.
Water resources face a significant environmental challenge: pollution from microplastics (MP) and heavy metals (HM). These elements pose a dual threat to ecosystems and public health. Microplastics, defined as particles smaller than 5 mm, are of anthropogenic origin, resulting from the degradation of plastics by environmental factors such as solar radiation and friction with the surrounding environment, as well as from their addition to cosmetic and textile products. These materials have been widely detected in drinking water and everyday foods. Heavy metals, high-density elements (>5g/cm3), while naturally present in the Earth's crust, are also generated in large quantities through human activity. Their toxicological risk lies in their ability to accumulate and efficiently move through the trophic chain. Due to the risks to public health and the impacts these pose to ecosystems, it is necessary to continue seeking solutions that enable their monitoring and detection. As a contribution, this work presents a methodology for detecting microplastics and heavy metals in seawater using different machine learning models and an electronic tongue coupled to a sensor network. Two different types of heavy metals, primarily zinc (Zn) and cadmium (Cd), as well as microplastic particles composed of expanded polystyrene (EPS), were detected under controlled conditions simulating different types of water. Atomic absorption spectroscopy (AAS) confirmed the concentrations of the heavy metals studied, supporting machine-learning classification of contaminated waters. Microplastics exhibited strong metal adsorption, influenced by the physicochemical properties of the water. Overall, AUC values above 90% were obtained for seven different models, demonstrating the reliability of the electronic tongue in conjunction with classical machine learning techniques for detecting these elements.