Spectroscopy and Spectral Analysis, Volume. 32, Issue 12, 3179(2012)
Classification of Plastics with Laser-Induced Breakdown Spectroscopy Based on Principal Component Analysis and Artificial Neural Network Model
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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WANG Qian-qian, HUANG Zhi-wen, LIU Kai, LI Wen-jiang, YAN Ji-xiang. Classification of Plastics with Laser-Induced Breakdown Spectroscopy Based on Principal Component Analysis and Artificial Neural Network Model[J]. Spectroscopy and Spectral Analysis, 2012, 32(12): 3179
Received: May. 18, 2012
Accepted: --
Published Online: Jan. 14, 2013
The Author Email: Qian-qian WANG (qqwang@bit.edu.cn)