Enhancing LIBS analysis accuracy of C element in low-carbon alloy steel by automatic segmented modelling with nonlinear-regression-based spectral line selection and Mahalanobis distance kernel space classification

The instability of the Laser Induced Breakdown Spectroscopy (LIBS) spectral intensity amplitude of C element in low-carbon alloy steel greatly decreases the analysis accuracy.

Authors

Hui Lü (ORCID: https://orcid.org/0000-0001-5145-0093)
Fu Chang
Jianhong Yang (ORCID: https://orcid.org/0000-0001-5908-8041)
Baojia Du (ORCID: https://orcid.org/0000-0002-7034-4728)
Yongqi Zhang (ORCID: https://orcid.org/0000-0001-8013-847X)
Zhanxiang Wang (ORCID: https://orcid.org/0000-0001-5277-5009)
Shiheng Zhang (ORCID: https://orcid.org/0009-0002-9191-3202)

Institutions

Yuncheng University (CN)
Shenyang Aluminum & Magnesium Engineering & Research Institute (China) (CN)
China Academy of Launch Vehicle Technology (CN)
University of Science and Technology Beijing (CN)

Publication Details

Journal: Journal of Analytical Atomic Spectrometry
Published: 2025-01-01
DOI: https://doi.org/10.1039/d5ja00144g
Primary Topic: Laser-induced spectroscopy and plasma
Type: article
Field-Weighted Citation Impact: 0.00

Enhancing LIBS analysis accuracy of C element in low-carbon alloy steel by automatic segmented modelling with nonlinear-regression-based spectral line selection and Mahalanobis distance kernel space classification

Authors

Institutions

Publication Details

Enhancing LIBS analysis accuracy of C element in low-carbon alloy steel by automatic segmented modelling with nonlinear-regression-based spectral line selection and Mahalanobis distance kernel space classification

Enhancing LIBS analysis accuracy of C element in low-carbon alloy steel by automatic segmented modelling with nonlinear-regression-based spectral line selection and Mahalanobis distance kernel space classification

Abstract

Ask Laika to Summarize, Analyze, and Connect papers live on the map.