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Fast Detection and Classification of Microplastics below 10 μm Using CNN with Raman Spectroscopy
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Researchers combined artificial intelligence with Raman spectroscopy to rapidly detect and classify microplastic particles smaller than 10 micrometers -- a size range that is especially concerning because these tiny particles can penetrate human tissues. The AI-based method dramatically reduced the time needed to identify plastic types compared to traditional approaches, making it more practical to monitor the smallest and most potentially harmful microplastics.
In light of the growing awareness regarding the ubiquitous presence of microplastics (MPs) in our environment, recent efforts have been made to integrate Artificial Intelligence (AI) technology into MP detection. Among spectroscopic techniques, Raman spectroscopy is preferred for the detection of MP particles measuring less than 10 μm, as it overcomes the diffraction limitations encountered in Fourier transform infrared (FTIR). However, Raman spectroscopy's inherent limitation is its low scattering cross section, which often results in prolonged data collection times during practical sample measurements. In this study, we implemented a convolutional neural network (CNN) model alongside a tailored data interpolation strategy to expedite data collection for MP particles within the 1-10 μm range. Remarkably, we achieved the classification of plastic types for individual particles with a mere 0.4 s of exposure time, reaching an approximate confidence level of 85.47(±5.00)%. We postulate that the result significantly accelerates the aggregation of microplastic distribution data in diverse scenarios, contributing to the development of a comprehensive global microplastic map.
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