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Machine learning-integrated droplet microfluidic system for accurate quantification and classification of microplastics
Water Research ( IF 11.4 ) Pub Date : 2025-01-17 , DOI: 10.1016/j.watres.2025.123161
Ji Woo Jeon, Ji Wook Choi, Yonghee Shin, Taewook Kang, Bong Geun Chung

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.

中文翻译:


机器学习集成液滴微流控系统,用于塑料微粒的准确定量和分类



微塑料 (MP) 污染会带来严重的环境和公共卫生问题,需要有效的检测方法。传统技术具有劳动密集型工作流程和复杂仪器的局限性,阻碍了快速的现场现场分析。在这里,我们介绍了机器学习 (ML) 集成的基于 Droplet 的 MP 实时分析 (MiDREAM) 系统。利用紧凑的蠕动泵,该系统实现了高通量液滴生成 (> 200 Hz),同时将 MP 封装在均匀的液滴 (142 ± 8 μm) 中。采用高分辨率互补金属氧化物半导体 (CMOS) 传感器与优化的 YOLO v8 ML 模型相结合进行实时分析,实现了 0.982 的平均精度 (mAP) 和 97.64% 的曲线下面积 (AUC)。血细胞计数器计数和表面增强拉曼光谱 (SERS) 的比较分析表明该系统具有卓越的性能,表现出高相关性 (R² = 0.9965) 和与理论值的最小偏差 (6.36 %)。该系统对不同大小的 MP 进行了准确分类,对 3、5、10、30 和 50 μm 颗粒的准确度分别为 95.4 %、87.9 %、95.3 %、85.3 % 和 92.5 %。使用真实水样的验证证实了系统的适应性,同时保持了高检测精度 (> 90 %)。MiDREAM 系统的现场现场测试也证明了其在各种环境中进行环境监测的稳健性能。因此,我们的便携式集成 MiDREAM 系统为实时环境监测应用提供了有前途的解决方案。
更新日期:2025-01-17
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