A mechanism study on laser-induced breakdown spectroscopy and machine learning-based characterization method for waste organic polymers.

The method based on machine learning and laser-induced breakdown spectroscopy (LIBS) is effective for rapid characterization of waste organic polymers (WOP). However, the lack of mechanistic interpretability leads to raises concerns regarding its reliability in practical applications. This study systematically investigated the fundamental chemical correlations between WOP fuel properties and LIBS spectral features through feature selection and machine learning interpretability analysis. Thirteen radical-associated key peaks were selected and strategically categorized into two groups for model construction. Under optimal conditions, the prediction accuracy for carbon, hydrogen, oxygen content and lower heating value (LHV) reach 97.74%, 91.22%, 91.28% and 97.02%, respectively. Notably, models utilizing 10 selected key peaks demonstrated superior performance compared to those employing raw LIBS spectra or principal components, especially with the absolute difference reaching 14.57% for O content prediction. Interpretability analysis showed that C2 swan bands had highest effects impacts on carbon, oxygen content and LHV prediction, whereas H I line was essential for hydrogen content prediction. This mechanistic investigation provided theoretical validation for LIBS-based rapid characterization systems, facilitating their practical implementation in downstream energy recovery processes. The established methodology offers a scientific foundation for advancing sustainable waste management and promoting circular economy development through efficient resource utilization.