Abstract

Currently, spectral analysis methods used in the classification of plastics have limitations that do not apply to opaque plastics or the stability of experimental results is not strong. In this paper, X-ray absorption spectroscopy (XAS) has been applied to classify plastics due to its strong penetrability and stability. Fifteen kinds of plastics are selected as specimens. X-ray, which is excited by a voltage of 60 kV, penetrated these specimens. The spectral data acquired by CdTe X-ray detector are processed by principal component analysis (PCA) and other data analysis methods. Then the back propagation neural networks (BPNN) algorithm is used to classify the processed data. The average recognition rate reached 96.95% and classification results of all types of plastic results were analyzed in detail. It indicates that XAS has the potential to classify plastics and that XAS can be used in some fields such as plastic waste sorting and recycling. At the same time, the technology of XAS, in the future, can also be used to classify more substances.

© 2017 The Author(s)

Accuracy improvement in plastics classification by laser-induced breakdown spectroscopy based on a residual network

Xinying Peng, Bohan Xu, Zhiying Xu, Xiaotao Yan, Ning Zhang, Yuzhi Qin, Qiongxiong Ma, Jiaming Li, Nan Zhao, and Qingmao Zhang
Opt. Express 29(21) 33269-33280 (2021)

Classification of steel using laser-induced breakdown spectroscopy combined with deep belief network

Guanghui Chen, Qingdong Zeng, Wenxin Li, Xiangang Chen, Mengtian Yuan, Lin Liu, Honghua Ma, Boyun Wang, Yang Liu, Lianbo Guo, and Huaqing Yu
Opt. Express 30(6) 9428-9440 (2022)

Parameter optimization and real-time calibration of a measurement-device-independent quantum key distribution network based on a back propagation artificial neural network

Feng-Yu Lu, Zhen-Qiang Yin, Chao Wang, Chao-Han Cui, Jun Teng, Shuang Wang, Wei Chen, Wei Huang, Bing-Jie Xu, Guang-Can Guo, and Zheng-Fu Han
J. Opt. Soc. Am. B 36(3) B92-B98 (2019)

Pattern recognition of visible and near-infrared spectroscopy from bayberry juice by use of partial least squares and a backpropagation neural network

Haiyan Cen, Yidan Bao, and Yong He
Appl. Opt. 45(29) 7679-7683 (2006)

Measuring parameters of laser self-mixing interferometry sensor based on back propagation neural network

Lei An and Bin Liu
Opt. Express 30(11) 19134-19144 (2022)