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Advancing automated microplastic detection: validation of a method using particle detection software and µ-Raman spectroscopy for 20 – 53 µm particles
Summary
Scientists have developed a faster, cheaper way to detect and count tiny microplastic particles (as small as 20 millionths of a meter, smaller than the width of a human hair) using a laser-based technology called Raman spectroscopy. This matters because these ultra-small plastic fragments are the ones most likely to enter our bodies and tissues, but until now they've been hard to accurately measure, making it difficult for researchers to understand how much of this material we're actually exposed to. While the method still needs refinement, it's a promising step toward better tools for tracking microplastic contamination in our food, water, and environment.
Research on microplastics is developing rapidly, but methods for quantifying the smallest particles (1-100 µm) remain limited. Automated spectroscopic methods using µ-FTIR and µ-Raman spectroscopy have been developed, but these methods are often time-consuming, require expensive filters, or show poor recovery of smaller particles. To address these problems, and as part of a larger effort to develop a cost-effective method for the rapid quantification of microplastic particles, we developed and validated a method for quantifying 20 – 53 µm microplastic fragments using µ-Raman spectroscopy on polycarbonate filters. Our method is fast, reliable, and does not require expensive consumables. We validated a method for polyethylene, polystyrene, and polyethylene terephthalate fragments with good precision for one sample run multiple times (~3 – 7% coefficient of variation) and independent samples of the same mass (~18 – 35%, depending on polymer). Recovery, tested across a larger size range of particles (up to 106 µm), ranged from 69 – 77% across polymers. KOH treatment (10 or 20%) reduced the recovery of our small polyethylene terephthalate particles. Our method proves promising, and we will continue its development and validation across a greater diversity of polymers, sizes and shapes, as well as with weathered particles extracted from environmental matrices.