Efficient and anti-interference plastic classification method suitable for one-shot learning based on laser induced breakdown spectroscopy

Abstract

Efficient recycling of plastics is critical for environmental sustainability. In this work, an efficient and anti-interference method for plastic classification based on one-shot learning and laser-induced breakdown spectroscopy (LIBS) was proposed. A residual neural network model with full-spectrum training (ResNet-FST) was developed based on convolutional neural networks, achieving an accuracy of 99.65 % in one-shot learning classification. A multi-parameter peak search algorithm was employed to extract key spectral features, and a linear residual classification model with peak auto-search (LRC-PAS) was developed to further enhance efficiency. The number of residual blocks and neurons was optimized to 2 and 80, respectively. Compared with ResNet-FST, LRC-PAS significantly improved classification efficiency. The mechanism underlying the spectral interference caused by plastic additives in LRC-PAS was elucidated. The anti-interference of additives in LRC-PAS was achieved with high accuracy. The results demonstrated that the proposed method achieves highly efficient and anti-interference classification of plastics, demonstrating great potential for real-time classification in the recycling industry.