| 光谱学与光谱分析 |
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| Classification of Plastics with Laser-Induced Breakdown Spectroscopy Based on Principal Component Analysis and Artificial Neural Network Model |
| WANG Qian-qian, HUANG Zhi-wen, LIU Kai, LI Wen-jiang, YAN Ji-xiang |
| School of Optoelectronics, Beijing Institute of Technology, Beijing 100081, China |
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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.
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Received: 2012-05-18
Accepted: 2012-09-18
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Corresponding Authors:
WANG Qian-qian
E-mail: qqwang@bit.edu.cn
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