scholarly journals Improvement of the performances of a commercial hand-held laser-induced breakdown spectroscopy instrument for steel analysis using multiple artificial neural networks

2020 ◽  
Vol 91 (7) ◽  
pp. 073111 ◽  
Author(s):  
F. Poggialini ◽  
B. Campanella ◽  
S. Legnaioli ◽  
S. Pagnotta ◽  
S. Raneri ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Laser Induced Breakdown Spectroscopy ◽  
Breakdown Spectroscopy ◽  
Laser Induced Breakdown ◽  
Steel Analysis ◽  
Artificial Neural

A Comparative Study of Two Data Reduction Methods for Steel Classification Based on LIBS

2014 ◽  
Vol 644-650 ◽  
pp. 4722-4725 ◽  
Author(s):  
Hai Yang Kong ◽  
Lan Xiang Sun ◽  
Jing Tao Hu ◽  
Yong Xin ◽  
Zhi Bo Cong
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Principal Component ◽  
Laser Induced Breakdown Spectroscopy ◽  
Breakdown Spectroscopy ◽  
Characteristic Lines ◽  
Laser Induced Breakdown ◽  
Reduction Methods ◽  
Artificial Neural ◽  
Priori Knowledge

Spectra of 27 steel samples were acquired by Laser-Induced Breakdown Spectroscopy (LIBS) for steel classification. Two methods were used to reduce dimensions: the first is to select characteristic lines of elements contained in the samples manually and the second is to do principal component analysis (PCA) of original spectra. Then the data after reducing dimensions was used as the input of artificial neural networks (ANN) to classify steel samples. The results show that, the better result can be achieved by selecting peak lines manually, but this solution needs much priori knowledge and wastes much time. The principal components (PCs) of original spectra were utilized as the input of artificial neural networks can also attain a good result nevertheless and this method can be developed into an automatic solution without any priori knowledge.

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