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

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 pa...

Full description

Saved in:
Bibliographic Details
Published inPlasma science & technology Vol. 17; no. 11; pp. 964 - 970
Main Author 孔海洋 孙兰香 胡静涛 辛勇 丛智博
Format Journal Article
LanguageEnglish
Published 01.11.2015
Subjects
Classification
Laser induced breakdown
Partitions
Spectra
Spectral lines
Spectroscopy
Steels
主成分分析
人工神经网络
光谱分析
光谱数据
击穿
分类
激光诱导
钢
Online AccessGet full text
ISSN1009-0630
DOI10.1088/1009-0630/17/11/14

Cover

More Information
Summary: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.
Bibliography:KONG Haiyang, SUN Lanxiang, HU Jingtao, XIN Yong, CONG Zhibo （1Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China; 2University of Chinese Academy of Sciences, Beijing 100049, China; 3CAS Key Laboratory of Networked Control System, Shenyang 110016, China）
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.
34-1187/TL
laser-induced breakdown spectroscopy, classification of steel samples, principal component analysis, artificial neural networks, selection of spectral data
ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:1009-0630
DOI:10.1088/1009-0630/17/11/14