用荧光成像与多光谱分析，高效识别环境中的微塑料颗粒。
Shedding Light on the Polymer's Identity: Microplastic Detection and Identification Through Nile Red Staining and Multispectral Imaging (FIMAP)
- 基于尼罗红染色和多光谱成像，构建自动检测平台FIMAP。
- 对大于3.14毫米的微塑料分类准确率达90%，召回率100%。
- 适合大规模环境样本筛查，尤其适用于大尺寸微塑料分析。
微塑料在环境中的广泛分布给其检测与识别带来挑战。荧光成像因其能增强颗粒可探测性并基于荧光特性实现精准分类而成为有前景的技术。然而传统分割方法存在信噪比低、光照不均、阈值设定困难及天然有机物（NOM）导致误检等问题。为此，本研究提出荧光成像微塑料分析平台（FIMAP），采用改装后的多光谱相机，配备四组光学滤镜和五种激发波长，可全面表征十种尼罗红染色微塑料（HDPE、LDPE、PP、PS、EPS、ABS、PVC、PC、PET、PA）的荧光行为，并有效排除NOM干扰。通过K均值聚类实现鲁棒分割（交并比=0.877），结合20维颜色坐标多元最近邻法进行分类，对大于3.14毫米的微塑料实现90%精度、90%准确率、100%召回率及94.7% F1分数；仅聚苯乙烯（PS）偶被误判为发泡聚苯乙烯（EPS）。对于35至104微米的小颗粒，准确率下降，可能源于染料吸附减少、可检测像素不足及相机稳定性问题。将FIMAP与高倍显微设备集成，有望进一步提升微塑料识别能力。该研究展示了一种自动化、高通量的环境样品中微塑料检测与分类框架。
原文摘要 · Abstract (English)
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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