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IdentifyingMulticomponent Microplastics in ComplexMatrices Using a Fast Fourier Convolutional Neural Network with HierarchicalFeature Mapping
Summary
Scientists have developed a smart AI tool that can quickly and accurately identify different types of plastic pollution mixed together in environmental samples, even when they're combined with dirt, additives, or other contaminants. This matters because knowing exactly what kinds of microplastics are in our environment (and eventually, in our food and water) is a crucial first step to understanding how they might affect our health—and this new method is faster and more accurate than previous approaches, correctly identifying plastics in real-world samples 100% of the time in tests.
Identifying environmental microplastics (EMPs) via Raman spectroscopy (RS) is well-advanced, whereas differentiating multicomponent MPs in impure mixtures remains challenging. In this work, we developed a Fast- Fourier-Convolutional neural network (FFCNN) to identify MP components in mixtures based on laser-confocal RS without complex isolation and introduced a hierarchical feature mapping (HFM) method to visualize and interpret the learned multilayer spectral features. The involved mixtures included several common types of MPs (polypropylene, polyethylene, polystyrene, polyvinyl chloride, and polyethylene terephthalate) and minor additives or impurities. The results indicated that the macro F1-score of the FFCNN was 93.6%, 10.18% higher than the second-place Random Forest, in the database with 3600 spectra from commercial MPs, mixed MPs, and EMPs. FFCNN achieved probability classification of MP mixtures via multilabel recognition. The HFM method visualized and tracked multilayer spectral feature evolution, revealing that FFC learned precise component signals through nonlocal receptive field and cross-scale fusion, eliminating noise to extract and integrate MPs-related characteristics in spectra. Of 22 environmental samples yielding one Raman spectrum each, MP component identification was 100% accurate, aligning with the pyrolysis-GC-MS cross-validation results. The work provides a practical framework for the rapid detection and identification of typical MP mixtures in the environment.