Optical Instruments, Volume. 45, Issue 5, 35(2023)

Research on classification of plastics by Raman spectroscopy combined with deep learning algorithm

Ningzhi YUAN, Shaohua CHEN*, and Taotao MU
Author Affiliations
  • College of Instrumental Science and Optoelectronic Engineering, Beijing Information Science and Technology University, Beijing 100192, China
  • show less

    Raman spectroscopy can identify the spectral characteristic peaks of plastic products, but the operation process is complicated and the accuracy needs to be improved. Therefore, a classification algorithm for plastic products based on one-dimensional convolution neural network (1-D CNN) is proposed. Firstly, data sets of 40 kinds of plastic packaging samples using polyethylene, polypropylene, polyethylene terephthalate and polystyrene as raw materials were established. Then, four algorithm models including 1-D CNN, KNN, DT and SVM were designed for training, and the spectral classification process, model accuracy and robustness were compared. The experimental results show that the classification accuracy of 1-D CNN can reach 98.62% without pretreatment. And the accuracy rate is 96.42% under 60 dB noise, which is better than the three traditional machine learning algorithm models. The results show that the multi-classification method of Raman spectral fusion neural network can improve the detection performance of plastic products.


    Get Citation

    Copy Citation Text

    Ningzhi YUAN, Shaohua CHEN, Taotao MU. Research on classification of plastics by Raman spectroscopy combined with deep learning algorithm[J]. Optical Instruments, 2023, 45(5): 35

    Download Citation

    EndNote(RIS)BibTexPlain Text
    Save article for my favorites
    Paper Information


    Received: Dec. 30, 2022

    Accepted: --

    Published Online: Dec. 27, 2023

    The Author Email: CHEN Shaohua (buaa38605@sina.com)