揭示聚合物的身份：通过尼罗红染色和多光谱成像（FIMAP）进行微塑料检测与识别
Shedding Light on the Polymer's Identity: Microplastic Detection and Identification Through Nile Red Staining and Multispectral Imaging (FIMAP)
AI总结：
本研究提出FIMAP多光谱成像平台，结合尼罗红染色与K-means分割及最近邻分类，实现对十种微塑料的高精度检测识别，精确率90%，F1分数94.7%。
摘要：
微塑料（MPs）在环境中的广泛分布对其检测和识别提出了重大挑战。荧光成像已成为一种有前景的技术，可增强塑料颗粒的可检测性，并基于荧光行为实现准确分类。然而，传统的分割技术面临诸多限制，包括信噪比差、照明不均匀、阈值化困难以及天然有机物质（NOM）引起的假阳性。为解决这些问题，本研究引入了荧光成像微塑料分析平台（FIMAP），该平台是一台经过改装的、配备四个光学滤光片和五个激发波长的多光谱相机。FIMAP能够全面表征十种尼罗红染色的微塑料（HDPE、LDPE、PP、PS、EPS、ABS、PVC、PC、PET和PA）的荧光行为，同时有效排除NOM。利用K-means聚类进行稳健分割（交并比=0.877），并采用20维颜色坐标多元最近邻方法进行微塑料分类（>3.14毫米），FIMAP实现了90%的精确率、90%的准确率、100%的召回率和94.7%的F1分数。仅PS偶尔被误分类为EPS。对于较小的微塑料（35-104微米），分类准确率下降，这可能是由于染色吸附减少、可检测像素较少以及相机不稳定性所致。将FIMAP与更高放大倍率的仪器（如显微镜）集成，可能会增强微塑料的识别能力。本研究将FIMAP呈现为一个自动化、高通量的框架，用于在大体积环境样本中检测和分类微塑料。
英文摘要：
The widespread distribution of microplastics (MPs) in the environment presents significant challenges for their detection and identification. Fluorescence imaging has emerged as a promising technique for enhancing plastic particle detectability and enabling accurate classification based on fluorescence behavior. However, conventional segmentation techniques face limitations, including poor signal-to-noise ratio, inconsistent illumination, thresholding difficulties, and false positives from natural organic matter (NOM). To address these challenges, this study introduces the Fluorescence Imaging Microplastic Analysis Platform (FIMAP), a retrofitted multispectral camera with four optical filters and five excitation wavelengths. FIMAP enables comprehensive characterization of the fluorescence behavior of ten Nile Red-stained MPs: HDPE, LDPE, PP, PS, EPS, ABS, PVC, PC, PET, and PA, while effectively excluding NOM. Using K-means clustering for robust segmentation (Intersection over Union = 0.877) and a 20-dimensional color coordinate multivariate nearest neighbor approach for MP classification (>3.14 mm), FIMAP achieves 90% precision, 90% accuracy, 100% recall, and an F1 score of 94.7%. Only PS was occasionally misclassified as EPS. For smaller MPs (35-104 microns), classification accuracy declined, likely due to reduced stain sorption, fewer detectable pixels, and camera instability. Integrating FIMAP with higher-magnification instruments, such as a microscope, may enhance MP identification. This study presents FIMAP as an automated, high-throughput framework for detecting and classifying MPs across large environmental sample volumes.
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