The Development and Prospect of Microplastic Detection and Analysis Technology

During recent years, nano-detection technology and machine learning-assisted identification methods have developed rapidly, providing new ideas for the precision, standardization and high-throughput quantification of microplastis detection. This article systematically reviews from early detection methods such as sieving and optical microscopy, to mainstream chemical identification techniques such as Fourier Transform infrared spectroscopy (FTIR), Raman spectroscopy, pyrolysis-gas chromatography/mass spectrometry (Py-GC/MS), and then to atomic force microscopy-infrared (AFM-IR), tip enhanced Raman spectroscopy (TERS), and machine learning. The evolution process and technical advantages and disadvantages of cutting-edge methods such as deep learning-assisted automatic recognition are analyzed, with a focus on core challenges such as matrix interference in complex environments, bottlenecks in nanoplastic detection, and the lack of method standardization. Research shows that a single technology is difficult to meet the comprehensive demands of high- throughput, nanoscale, and on-site rapid detection, while the integration of multimodal technologies and artificial intelligence-driven intelligent analysis will become the breakthrough direction. It is necessary to accelerate the establishment of a globally shared spectral database, unified operation norms, and the research and development of low-cost portable devices, in order to achieve efficient, accurate and comparable monitoring of microplastics in environmental samples, and provide solid technical support for the prevention.