title: [Classification of plastics with laser-induced breakdown spectroscopy based on principal component analysis and artificial neural network model].
abstract: The classification of seven kinds of plastic (ABS, PET, PP, PS, PVC, HDPE and PMMA) with the laser-induced breakdown spectroscopy based on artificial neural network model was investigated in the present paper. One hundred seventy LIBS spectra for each type of plastic were collected. Firstly, all 1 190 plastics LIBS spectra were studied with principal component analysis. The first five principal components (PC) totally explain 78.4% of the original spectrum information. Therefore, the scores of five PCs of 130 LIBS spectra for each kind of plastic were chosen as the training set to build a back-propagation artificial network model. And the other 40 LIBS spectra of each sample were used as the testing set for the trained model. The classification accuracy was 97.5%. Experimental results demonstrate that plastics can be classified by using principal component analysis and artificial neural network (BP) method.
authors: School of Optoelectronics, Beijing Institute of Technology, Beijing 100081, China. qqwang@bit.edu.cn
authors: Qian-Qian
authors: QQ
authors: Wang
authors: Zhi-Wen
authors: ZW
authors: Huang
authors: Kai
authors: K
authors: Liu
authors: Wen-Jiang
authors: WJ
authors: Li
authors: Ji-Xiang
authors: JX
authors: Yan