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Shedding light on the polymer’s identity: Microplastic detection and identification through nile red staining and multispectral imaging (FIMAP)
Summary
Researchers built a multispectral imaging platform called FIMAP that uses fluorescent dye and five different light wavelengths to automatically detect and classify ten types of microplastics with 90% accuracy, while effectively ignoring natural organic matter that typically causes false positives. The system provides a scalable, high-throughput approach for analyzing large environmental samples without needing expensive traditional instruments like infrared spectroscopy.
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 the detectability of plastic particles and enabling accurate classification based on fluorescence behavior. However, conventional image segmentation techniques for fluorescent particles face several limitations, including poor signal-to-noise ratio, inconsistent illumination, particle thresholding difficulties, and false positives from natural organic matter (NOM). To overcome these challenges, this study introduces the Fluorescence Imaging for Microplastic Analysis Platform (FIMAP), a retrofitted multispectral camera equipped with four distinct optical filters and excited at five different wavelengths. We demonstrate that FIMAP enables comprehensive characterization of the fluorescence behavior of ten Nile Red-stained MPs (HDPE, LDPE, PP, PS, EPS, ABS, PVC, PC, PET, PA) while effectively excluding NOM. This is achieved through K-means clustering for robust particle segmentation (Intersection over Union = 87.7%) and a 20-dimensional color coordinate multivariate nearest neighbor approach for MP classification (>3.14 mm), yielding a precision of 90%, accuracy of 90%, recall of 100%, and an F1 score of 94.7%. Among the ten MPs, only PS was occasionally misclassified as its expanded form (EPS). For smaller MPs (35–104 μm), classification accuracy declined, likely due to reduced fluorescent stain sorption, fewer detectable pixels, and camera instability. However, integrating FIMAP with higher-magnification instruments, such as a microscope, may enhance MP identification accuracy. In summary, FIMAP introduces an automated, high-throughput framework for the comprehensive detection and classification of MPs across large environmental sample volumes. • FIMAP enables automated detection and classification of Nile Red-stained MPs. • K-means clustering improves segmentation, reducing false positives from NOM. • Multispectral imaging (5 excitations, 4 filters) reveals distinct MP fluorescence patterns. • Nearest neighbor search achieves 90% precision and classification accuracy for 10 MPs. • FIMAP provides a scalable solution for high-throughput environmental MP analysis.