Optical Instruments, Volume. 45, Issue 5, 35(2023)
Research on classification of plastics by Raman spectroscopy combined with deep learning algorithm
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.
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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
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Received: Dec. 30, 2022
Accepted: --
Published Online: Dec. 27, 2023
The Author Email: CHEN Shaohua (buaa38605@sina.com)