Challenges in sorting unknown post-consumer plastics using LIBS combined with machine learning
Abstract
The rampant use of plastics and insufficient disposal practices have put the environment and ecosystems at risk, necessitating efficient sorting methods for recycling. This study demonstrates the potential of a LIBS–ML system for real-time classification of post-consumer plastics using “real, as-received samples.” Using a rigorous LOSO strategy, the evaluation closely simulated realistic recycling conditions. The ANN achieved excellent accuracy for PET, PS, and PVC, while the classification of the HDPE–LDPE–PP group remains a major challenge due to their very similar chemical structures and the large variations present in such real-world materials. Among various analytical strategies compared, the PCA–ANN provided the highest accuracy (99.9%), and LDA–ANN offered nearly comparable performance (98%) with better computational efficiency. All trained models required
Fields of Study & Subjects
Laser-induced spectroscopy and plasma
Mass Spectrometry Techniques and Applications
Advanced Proteomics Techniques and Applications
Publication details
- Journal / source
- Journal of the Optical Society of America B
- Publisher
- Optica Publishing Group
- Volume
- 43
- Issue
- 8
- Pages
- B34–B34
- Publication date
- 2 February 2026
- Language
- EN