Classification and identification of brands of iron ores using laser-induced breakdown spectroscopy combined with principal component analysis and artificial neural networks

2020 ◽  
Vol 12 (10) ◽  
pp. 1316-1323 ◽  
Author(s):  
Yawen Yang ◽  
Chen Li ◽  
Shu Liu ◽  
Hong Min ◽  
Chenglin Yan ◽  
...  
Keyword(s):  
Neural Networks ◽  
Principal Component Analysis ◽  
Artificial Neural Networks ◽  
Principal Component ◽  
Component Analysis ◽  
Laser Induced Breakdown Spectroscopy ◽  
Iron Ores ◽  
Breakdown Spectroscopy ◽  
Laser Induced Breakdown ◽  
Ann Models

In this work, PCA-ANN models of LIBS spectra were developed to classify and identify iron ores according to the production countries and brands.

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.

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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