Microplastic particles (MPs) are emerging environmental contaminants that can adsorb toxic metals and organic species, posing risks to ecosystems and human health. In this study, a compact analytical platform combining laser-induced breakdown spectroscopy (LIBS) and Raman spectroscopy (RS) was developed for the simultaneous molecular and elemental analysis of polymer microbeads. The system shares a common optical path for consecutive acquisition of LIBS and Raman signals from the same spot, enabling direct correlation between polymer identity and adsorbed metal content. Characteristic Raman bands successfully discriminated polystyrene (PS), polyethylene (PE), and polypropylene (PP), while LIBS analysis of metal-exposed PS beads revealed distinct Pb I (405.8 nm) and Cu I (324.8 nm) lines with concentration-dependent intensities. Quantitative calibration yielded limits of detection of 2.29 ppm for Pb and 1.61 ppm for Cu, based on the Pb I 405.8 nm and Cu I 324.8 nm lines, respectively. Machine learning-based clustering of Raman spectra, including Gaussian mixture model and k-means approaches, achieved up to 99.3% unsupervised classification accuracy. The results demonstrate that the integrated LIBS-RS system provides analytical performance comparable to laboratory-scale LIBS instruments, while offering the added capability of molecular fingerprinting for field-deployable microplastic and contaminant monitoring.
Shin et al. (Thu,) studied this question.