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Quantification of PP, PE, and PET microplastics in Lake water by Raman spectroscopy combined with PLS regression
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Researchers developed an integrated analytical approach using Raman spectroscopy combined with partial least squares regression for simultaneously detecting and quantifying polypropylene, polyethylene, and PET microplastics in lake water. The method achieved reliable quantification at trace-level concentrations in complex water matrices. The study provides a practical tool for environmental monitoring that could improve the accuracy and efficiency of microplastic measurements in freshwater systems.
Microplastics are pervasive contaminants in aquatic ecosystems, posing significant ecological and health risks. Their accurate quantification in complex water matrices remains challenging, requiring advanced analytical techniques. This study develops an integrated analytical approach using Raman spectroscopy and chemometrics for the simultaneous detection and quantification of trace-level polypropylene (PP), polyethylene (PE), and polyethylene terephthalate (PET) microplastics in lakes. A dedicated sample preparation protocol was established to overcome microplastic hydrophobicity and low abundance, employing optimized solvent dispersion and enrichment to enable highly accurate quantification via Partial Least Squares (PLS) regression. The resulting models demonstrated exceptional performance: the Smooth-WT-CARS-PLS (Smooth-Wavelet Transform-Competitive Adaptive Reweighted Sampling-Partial Least Squares) model for PP achieved a coefficient of determination for prediction (R) of 0.9807 and a mean relative error of prediction (MRE) of 0.0599; the Smooth-CARS-PLS (Smooth-Competitive Adaptive Reweighted Sampling-Partial Least Squares) model for PE attained an R of 0.9899 with an MRE of 0.0880; and the WT-Smooth-CARS-PLS (Wavelet Transform-Smooth-Competitive Adaptive Reweighted Sampling-Partial Least Squares) model for PET yielded an R of 0.9822 and an MRE of 0.0968. Furthermore, the method showed low detection limits (0.31 μg/mL for PP, 0.25 for PE, 0.13 for PET), confirming its high sensitivity. Practical applicability was validated using real lake samples, and the models maintained low prediction errors when applied to different lake environments, demonstrating satisfactory robustness. This reliable framework provides an efficient solution for rapid and precise monitoring of microplastics in complex waters, showing great promise for supporting future environmental risk assessments and regulatory efforts.
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