Accuracy improvement of iron ore analysis using laser-induced breakdown spectroscopy with a hybrid sparse partial least squares and least-squares support vector machine model-Reference-Cited by-同舟云学术

Accuracy improvement of iron ore analysis using laser-induced breakdown spectroscopy with a hybrid sparse partial least squares and least-squares support vector machine model

Published:2018 Issue:8 Volume:33 Page:1330-1335
ISSN:0267-9477
Container-title:Journal of Analytical Atomic Spectrometry
language:en
Short-container-title:J. Anal. At. Spectrom.

Author:

Guo Y. M.¹²³⁴,Guo L. B.¹²³⁴,Hao Z. Q.¹²³⁴^ORCID,Tang Y.¹²³⁴,Ma S. X.¹²³⁴,Zeng Q. D.¹²³⁴⁵,Tang S. S.¹²³⁴,Li X. Y.¹²³⁴^ORCID,Lu Y. F.¹²³⁴^ORCID,Zeng X. Y.¹²³⁴

Affiliation:

1. Wuhan National Laboratory for Optoelectronics (WNLO)

2. Huazhong University of Science and Technology

3. Wuhan

4. P. R. China

5. School of Physics and Electronic-information Engineering

Abstract

A hybrid sparse partial least squares and least-squares support vector machine model was proposed to improve the accuracy of iron ore analysis using LIBS.

Funder

National Natural Science Foundation of China

Publisher

Royal Society of Chemistry (RSC)

Subject

Spectroscopy,Analytical Chemistry

Link

http://pubs.rsc.org/en/content/articlepdf/2018/JA/C8JA00119G

Reference33 articles.

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

2. Laser-induced breakdown spectroscopy in China

3. Wavelet-based interference correction for laser-induced breakdown spectroscopy

4. Detection of carbon content in a high-temperature and high-pressure environment using laser-induced breakdown spectroscopy

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