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

Abstract

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.

Keywords

• Laser-induced breakdown spectroscopy
• Plastics classification
• Neighborhood component analysis
• Principal component analysis
• Support vector machine