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High-frequency ultrasound combined with deep learning enables identification and size estimation of microplastics
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Scientists developed a new method using sound waves and artificial intelligence to quickly detect tiny plastic particles (microplastics) in the environment with over 97% accuracy. This technology could help us better monitor microplastic pollution in water and food sources, which is important since these particles can end up in our bodies through what we eat and drink. The new method is much faster than current testing approaches, making it easier to track plastic pollution on a large scale.
Microplastics are widespread in aquatic and terrestrial environments, yet standard identification techniques remain slow, labor-intensive, and unsuitable for large-scale or in situ monitoring. In this work, we investigate high-frequency ultrasound as a fast, non-destructive alternative for microplastic detection, material identification, and size estimation. A peak-based extraction method isolated particle-specific echoes, from which temporal and spectral features were computed. We evaluated several machine learning methods and introduced a one-dimensional convolutional neural network (1D-CNN) to classify material types. The proposed 1D-CNN achieved 97.14% accuracy, outperforming traditional models. Particle size was further estimated using material-specific multilayer perceptrons, which classified microspheres into four size ranges with an average accuracy of 99.93%. These results show that high-frequency ultrasound encodes discriminative scattering patterns that can be learned directly from raw acoustic signals, offering a fast and scalable framework for microplastic characterization with potential for future real-time or in situ applications.
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