Shedding Light on the Polymer's Identity: Microplastic Detection and Identification Through Nile Red Staining and Multispectral Imaging (FIMAP)

TL;DR

This paper introduces FIMAP, a multispectral fluorescence imaging platform that improves detection and classification of microplastics in environmental samples using Nile Red staining and advanced segmentation and classification techniques.

Contribution

FIMAP is a novel multispectral imaging system that enhances microplastic detection and identification, overcoming limitations of conventional methods with high accuracy and automation.

Findings

Achieved 90% precision and 94.7% F1 score in microplastic classification.

Effectively excluded natural organic matter from analysis.

Performance declines for smaller microplastics due to reduced staining and detection.

Abstract

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.