Identification of different colored plastics by laser-induced breakdown spectroscopy combined with neighborhood component analysis and support vector machine

Polymer Testing

Xuelin Wen¹  Lianbo Guo²  Deng Zhang³  Jinling Xiao³  Feng Chen³  Weiliang Wang³  Yanwu Chu³  Zhenlin Hu³  Xuechen Niu³  Junfei Nie³ 

[1] Hunan Provincial Key Laboratory of Girds Operation and Control on Multi-Power Sources Area, Shaoyang University, Shaoyang, Hunan, 422000, China;School of Optical and Electronic Information, Huazhong University of Science and Technology, Wuhan, Hubei, 430074, China;Wuhan National Laboratory for Optoelectronics (WNLO), Huazhong University of Science and Technology, Wuhan, Hubei, 430074, China;

关键词: Laser-induced breakdown spectroscopy;    Plastics classification;    Neighborhood component analysis;    Principal component analysis;    Support vector machine;   

DOI  :  

来源: DOAJ

【 摘 要 】

Plastic recycling is an effective strategy to solve the shortage of national resources and improve the ecological environment. Herein, a novel approach was proposed to identify different colored plastics using laser-induced breakdown spectroscopy (LIBS) by neighborhood component analysis (NCA) and support vector machine (SVM). Six kinds of plastics (PVC, POM, ABS, PP, PA, and PE) with multiple colors were used to verify the feasibility of this method. Firstly, the types of plastics were classified by SVM, and the average accuracy about 97% was obtained. Then the same type of plastics with multiple colors was classified by SVM, and more than 99% average accuracy was acquired. However, the average accuracy of PVC by SVM was only 82%. To improve the average identification accuracy of PVC, the neighborhood component analysis (NCA) was used for feature selection by evaluating the weights of spectral lines. The spectral lines of focus elements (hydrogen (H), potassium (K), carbon (C), etc.) with higher weight were used as the input of SVM. The average accuracy of NCA-SVM was 91%, which higher than 9% and 5% with SVM and principal component analysis (PCA) combined with SVM (PCA -SVM), respectively. The results demonstrated that LIBS with the SVM and NCA-SVM can acquire high accuracy identification of different plastics, as well as recognition of the same type of plastics with different colors.

【 授权许可】

Unknown