A Novel LIBS-Machine Learning Strategy for Multimetal Detection in Microsized PMMA Particles: Efficient Quantification for Composite Pollution.

Authors

Rongling Zhang, Chenjia Song, Jingzhong Liu, Tianlong Zhang, Hongsheng Tang, Hua Li

Published in

Analytical chemistry. Jun 30, 2025. Epub Jun 30, 2025.

Abstract

Microplastics (MPs) have emerged as a critical environmental pollutant, causing composite pollution through their widespread production, usage, and disposal, as well as their capacity to carry other contaminants such as heavy metals. This study presents a novel approach combining laser-induced breakdown spectroscopy (LIBS) with machine learning for the simultaneous quantitative detection of three metals (Cr, Pb, and Cu) in 25 contaminated poly(methyl methacrylate) (PMMA) samples with a diameter of 2 μm. The effects of different preprocessing methods and variable selection techniques on the predictive performance of partial least-squares (PLS) calibration models were investigated. Based on optimized input variables and model parameters, PLS calibration models were developed using mean relative error (MRE), root-mean-square error (RMSE), and coefficient of determination (R²) as evaluation metrics. The models of standard normal variate-competitive adaptive reweighted sampling-PLS (SNV-CARS-PLS) for Cr and Pb, and wavelet transform-CARS-PLS (WT-CARS-PLS) for Cu, demonstrated superior correlation relationships (Cr: R_p² = 0.9750, Pb: R_p² = 0.9759, Cu: R_p² = 0.9088) compared to univariate calibration methods. The values of RMSE_p for Cr, Pb, and Cu decreased by 5.495, 9.170, and 3.765 ppm, respectively, while values of MRE_p decreased by 71.73%, 65%, and 66.81%, respectively. The values of ratio of prediction to deviation (RPD) for three models in -leave-one-out cross-validation (LOOCV) were 20.4, 31.6, and 31.6 respectively. Furthermore, the limits of detection (LODs) for the three heavy metal elements were ≤1.534 ppm. The SNV/WT-CARS-PLS method significantly improved quantitative analysis accuracy, providing essential theoretical and technical support for composite pollution monitoring and prevention in MPs.

PMID:
40586477
Bibliographic data and abstract were imported from PubMed on 01 Jul 2025.

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