Conditional data processing for single-shot spectral analysis by use of laser-induced breakdown spectroscopy

2003 ◽  
Vol 42 (30) ◽  
pp. 6022 ◽  
Author(s):  
Jorge E. Carranza ◽  
Kenjiro Iida ◽  
David W. Hahn
Keyword(s):  
Spectral Analysis ◽  
Data Processing ◽  
Laser Induced Breakdown Spectroscopy ◽  
Single Shot ◽  
Breakdown Spectroscopy ◽  
Laser Induced Breakdown

scholarly journals Laser-Induced Breakdown Spectroscopy and Principal Component Analysis for the Classification of Spectra from Gold-Bearing Ores

2019 ◽  
Vol 74 (1) ◽  
pp. 42-54 ◽  
Author(s):  
Daniel Diaz ◽  
Alejandro Molina ◽  
David W. Hahn
Keyword(s):  
Principal Component Analysis ◽  
Limit Of Detection ◽  
Principal Component ◽  
Component Analysis ◽  
Emission Lines ◽  
Laser Induced Breakdown Spectroscopy ◽  
Single Shot ◽  
Breakdown Spectroscopy ◽  
Laser Induced Breakdown

Laser-induced breakdown spectroscopy (LIBS) and principal component analysis (PCA) were applied to the classification of LIBS spectra from gold ores prepared as pressed pellets from pulverized bulk samples. For each sample, 5000 single-shot LIBS spectra were obtained. Although the gold concentrations in the samples were as high as 7.7 µg/g, Au emission lines were not observed in most single-shot LIBS spectra, rendering the application of the usual ensemble-averaging approach for spectral processing to be infeasible. Instead, a PCA approach was utilized to analyze the collection of single-shot LIBS spectra. Two spectral ranges of 21 nm and 0.15 nm wide were considered, and LIBS variables (i.e., wavelengths) reduced to no more than three principal components. Single-shot spectra containing Au emission lines (positive spectra) were discriminated by PCA from those without the spectral feature (negative spectra) in a spectral range of less than 1 nm wide around the Au(I) 267.59 nm emission line. Assuming a discrete gold distribution at very low concentration, LIBS sampling of gold particles seemed unlikely; therefore, positive spectra were considered as data outliers. Detection of data outliers was possible using two PCA statistical parameters, i.e., sample residual and Mahalanobis distance. Results from such a classification were compared with a standard database created with positive spectra identified with a filtering algorithm that rejected spectra with an Au intensity below the smallest detectable analytical LIBS signal (i.e., below the LIBS limit of detection). The PCA approach successfully identified 100% of the data outliers when compared with the standard database. False identifications in the multivariate approach were attributed to variations in shot-to-shot intensity and the presence of interfering emission lines.

Spectral analysis of Qinling Mountain rock using laser induced breakdown spectroscopy

2013 ◽  
Vol 60 (21) ◽  
pp. 1905-1909 ◽  
Author(s):  
W.F. Luo ◽  
X.X. Zhao ◽  
H.Y. Zhu ◽  
D.H. Xie ◽  
J. Liu ◽  
...  
Keyword(s):  
Spectral Analysis ◽  
Laser Induced Breakdown Spectroscopy ◽  
Breakdown Spectroscopy ◽  
Qinling Mountain ◽  
Laser Induced Breakdown ◽  
Mountain Rock

Coal analysis by laser-induced breakdown spectroscopy: a tutorial review

2019 ◽  
Vol 34 (6) ◽  
pp. 1047-1082 ◽  
Author(s):  
Sahar Sheta ◽  
Muhammad Sher Afgan ◽  
Zongyu Hou ◽  
Shun-Chun Yao ◽  
Lei Zhang ◽  
...  
Keyword(s):  
Fly Ash ◽  
Data Processing ◽  
Review Article ◽  
Laser Induced Breakdown Spectroscopy ◽  
Breakdown Spectroscopy ◽  
Coal Analysis ◽  
Analytical Results ◽  
Laser Induced Breakdown ◽  
Combustion Monitoring ◽  
Libs Applications

This review article forms a guideline for LIBS contribution in coal analysis, encompassing fundamental aspects, operation modes, data processing, and analytical results. LIBS applications related to coal utilization are also highlighted (fly ash analysis and combustion monitoring).

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