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

Journal: Chemosphere
Published Date: Apr 15, 2025

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.

Authors

  • Zhiying Xu
    State Key Laboratory of Nuclear Physics and Technology, and Key Laboratory of HEDP of the Ministry of Education, CAPT, Peking University, Beijing, 100871, China; Guangdong Institute of Laser Plasma Accelerator Technology, 510540, China.
  • Xingyu Zhao
    University of Science and Technology of China, Hefei, China; Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China.
  • Xinying Peng
    School of Physics, State Key Laboratory of Optoelectronic Materials and Technologies, Sun Yat-sen University, Guangzhou, 510275, China.
  • Kai Wang
    Department of Rheumatology, The Affiliated Huai'an No. 1 People's Hospital of Nanjing Medical University, Huai'an, Jiangsu, China.
  • Kedong Wang
    State Key Laboratory of Nuclear Physics and Technology, and Key Laboratory of HEDP of the Ministry of Education, CAPT, Peking University, Beijing, 100871, China; Guangdong Institute of Laser Plasma Accelerator Technology, 510540, China.
  • Nan Zhao
    CAS Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences, Beijing, China.
  • Jiaming Li
    Guangzhou Regenerative Medicine and Health Guangdong Laboratory, Guangzhou, China; Guangzhou Kangrui AI Technology Co. and Guangzhou HuiBoRui Biological Pharmaceutical Technology Co., Ltd, Guangzhou, China.
  • Qingmao Zhang
    Guangdong Provincial Key Laboratory of Nanophotonic Functional Materials and Devices, School of Information and Optoelectronic Science and Engineering, South China Normal University, Guangzhou 510006, China.
  • Xueqing Yan
    State Key Laboratory of Nuclear Physics and Technology, and Key Laboratory of HEDP of the Ministry of Education, CAPT, Peking University, Beijing, 100871, China; Guangdong Institute of Laser Plasma Accelerator Technology, 510540, China.
  • Kun Zhu
    Aviation Technology Research Institute, China Aerospace Science and Industry Corporation, Beijing, 100143, China.

Keywords

External Resources

Popular Topics
Recent Journals