Machine learning-integrated droplet microfluidic system for accurate quantification and classification of microplastics

Microplastic (MP) pollution poses serious environmental and public health concerns, requiring efficient detection methods. Conventional techniques have the limitations of labor-intensive workflows and complex instrumentation, hindering rapid on-site field analysis. Here, we present the Machine learning (ML)-Integrated Droplet-based REal-time Analysis of MP (MiDREAM) system. Utilizing a compact peristaltic pump, the system achieved high-throughput droplet generation (> 200 Hz) while encapsulating MPs in uniform droplets (142 ± 8 μm). A high-resolution complementary metal oxide semiconductor (CMOS) sensor combined with an optimized YOLO v8 ML model was employed for real-time analysis, achieving a mean average precision (mAP) of 0.982 and an area under the curve (AUC) of 97.64 %. Comparative analysis with hemocytometer counting and surface-enhanced Raman spectroscopy (SERS) demonstrated the superior performance of the system, demonstrating high correlation (R² = 0.9965) and minimal deviation (6.36 %) from theoretical values. The system accurately classified MPs of different sizes, achieving accuracies of 95.4 %, 87.9 %, 95.3 %, 85.3 %, and 92.5 % for 3, 5, 10, 30, and 50 μm particles, respectively. Validation with real-world water samples confirmed the system adaptability, while maintaining high detection accuracy (> 90 %). The on-site field tests of MiDREAM system also demonstrated its robust performance for environmental monitoring in a variety of environments. Therefore, our portable and integrated MiDREAM system offers a promising solution for real-time environmental monitoring applications.