Selection of Spectral Data for Classification of Steels Using Laser-Induced Breakdown Spectroscopy

作     者：孔海洋 孙兰香 胡静涛 辛勇 丛智博 

作者机构：Shenyang Institute of AutomationChinese Academy of Sciences University of Chinese Academy of Sciences CAS Key Laboratory of Networked Control System 

出 版 物：《Plasma Science and Technology》 (等离子体科学和技术（英文版）)

年 卷 期：2015年第17卷第11期

页      面：964-970页

核心收录：

学科分类：08[工学] 080502[工学-材料学] 0805[工学-材料科学与工程（可授工学、理学学位）] 

基　　金：supported by the National High Technology Research and Development Program of China(863 Program)(No.2012AA040608) National Natural Science Foundation of China(Nos.61473279,61004131) the Development of Scientific Research Equipment Program of Chinese Academy of Sciences(No.YZ201247) 

主　　题：laser-induced breakdown spectroscopy classification of steel samples principal component analysis artificial neural networks selection of spectral data 

摘      要：Principal component analysis （PCA） combined with artificial neural networks was used to classify the spectra of 27 steel samples acquired using laser-induced breakdown spectroscopy. Three methods of spectral data selection, selecting all the peak lines of the spectra, selecting intensive spectral partitions and the whole spectra, were utilized to compare the infiuence of different inputs of PCA on the classification of steels. Three intensive partitions were selected based on experience and prior knowledge to compare the classification, as the partitions can obtain the best results compared to all peak lines and the whole spectra. We also used two test data sets, mean spectra after being averaged and raw spectra without any pretreatment, to verify the results of the classification. The results of this comprehensive comparison show that a back propagation network trained using the principal components of appropriate, carefully selecred spectral partitions can obtain the best results accuracy can be achieved using the intensive spectral A perfect result with 100% classification partitions ranging of 357-367 nm.