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

• Published in: Plasma science & technology 2015-11, Vol.17 (11), p.964-970
• Main Author: 孔海洋 孙兰香 胡静涛 辛勇 丛智博
• Format: Article
• Language: English
• 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
• ISSN: 1009-0630
• DOI: 10.1088/1009-0630/17/11/14

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.