Classification and identification of brands of iron ores using laser-induced breakdown spectroscopy combined with principal component analysis and artificial neural networks-Reference-Cited by-同舟云学术

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

Published:2020 Issue:10 Volume:12 Page:1316-1323
ISSN:1759-9660
Container-title:Analytical Methods
language:en
Short-container-title:Anal. Methods

Author:

Yang Yawen¹²³⁴⁵,Li Chen⁵⁶⁴,Liu Shu⁵⁶⁴^ORCID,Min Hong⁵⁶⁴,Yan Chenglin⁵⁶⁴,Yang Minli¹²³⁴,Yu Jin⁷⁸⁹¹⁰

Affiliation:

1. College of Chemistry and Materials Science

2. Shanghai Normal University

3. Shanghai 200234

4. P. R. China

5. Technical Center for Industrial Product and Raw Material Inspection and Testing

6. Shanghai

7. School of Physics and Astronomy

8. Shanghai Jiao Tong University

9. Shanghai 200240

10. China

Abstract

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

Funder

National Basic Research Program of China

Publisher

Royal Society of Chemistry (RSC)

Subject

General Engineering,General Chemical Engineering,Analytical Chemistry

Link

http://pubs.rsc.org/en/content/articlepdf/2020/AY/C9AY02443C

Reference37 articles.

1. Laser-Induced Breakdown Spectroscopy

2. Laser-Induced Breakdown Spectroscopy (LIBS), Part I: Review of Basic Diagnostics and Plasma—Particle Interactions: Still-Challenging Issues within the Analytical Plasma Community

3. Laser-Induced Breakdown Spectroscopy (LIBS), Part II: Review of Instrumental and Methodological Approaches to Material Analysis and Applications to Different Fields

4. Evaluation of energy dispersive scanning electron microscopy and X-ray fluorescence techniques for analysis of compost quality

5. Quantitative mineralogy for facies definition in the Marcellus Shale (Appalachian Basin, USA) using XRD-XRF integration

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