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.
王茜蒨，黄志文，刘 凯，李文江，阎吉祥 . 基于主成分分析和人工神经网络的激光诱导击穿光谱塑料分类识别方法研究 [J]. 光谱学与光谱分析, 2012, 32(12): 3179-3182.
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 . SPECTROSCOPY AND SPECTRAL ANALYSIS, 2012, 32(12): 3179-3182.
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