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

Q: What type of study is this?

This is a Experimental Study study.

Shiheng Zhang; Hui Lü; Jianhong Yang; Fu Chang; Zhanxiang Wang; Yongqi Zhang; Baojia Du

doi:10.1039/d5ja00144g

January 1, 2025Journal of Analytical Atomic Spectrometry

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

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Key Points

The study finds that using automatic segmented modelling significantly enhances the analysis accuracy of carbon elements.
Nonlinear regression for spectral line selection is pivotal, improving detection reliability in low-carbon alloy steel.
Mahalanobis distance classification effectively reduces errors in LIBS spectral intensity amplitude for carbon analysis.
The method offers a promising approach for improving the reliability of LIBS in alloy steel applications.

Implication

This analysis improves LIBS analysis accuracy for carbon elements in low-carbon alloy steel, suggesting advanced modelling techniques are effective.

Abstract

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.

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Cite This Study

Zhang et al. (2025) studied this question.

synapsesocial.com/papers/68d45e4e31b076d99fa5e561 https://doi.org/10.1039/d5ja00144g

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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

Key Points

Implication

Abstract

Discussion

Cite This Study

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

Key Points

Implication

Abstract

Discussion

Cite This Study

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