Multimodal LIBS-FLIPA fusion with frame segmentation for robust plastic classification via advanced LIPA processing

The global increase in plastic waste, exceeding 400 million tons annually, underscores the urgent need for efficient plastic sorting and recycling. Laser-induced breakdown spectroscopy (LIBS) shows potential in this area, but its practical application is limited by challenges such as plasma fluctuations and low robustness. To address these limitations, we introduce laser-induced plasma acoustic (LIPA) signals and propose the frame-segmentation LIPA (FLIPA) algorithm to enhance LIBS analysis. This innovative algorithm reduces the number of variables in LIPA by 99% while optimizing computational efficiency and classification accuracy. Additionally, a multimodal fusion technique, LIBS-FLIPA, is developed to integrate LIBS and FLIPA at the feature level. The results indicate that LIBS-FLIPA significantly improves classification accuracy, robustness, and generalization, effectively mitigating overfitting risks. This study provides novel, to the best of our knowledge, solutions to challenges in LIBS analysis and proposes an innovative approach for robust plastic sorting, advancing the methodologies of LIBS research.