Assessment of the performance of quantitative feature-based transfer learning LIBS analysis of chromium in high temperature alloy steel samples-Reference-Cited by-同舟云学术

Assessment of the performance of quantitative feature-based transfer learning LIBS analysis of chromium in high temperature alloy steel samples

Published:2020 Issue:11 Volume:35 Page:2639-2648
ISSN:0267-9477
Container-title:Journal of Analytical Atomic Spectrometry
language:en
Short-container-title:J. Anal. At. Spectrom.

Author:

Chang Fu¹²³⁴,Lu Huili⁵²⁶⁴,Sun Hao¹²³⁴,Yang Jianhong¹²³⁴⁷^ORCID

Affiliation:

1. School of Mechanical Engineering

2. University of Science and Technology Beijing

3. Beijing

4. China

5. Basic Experimental Center of Natural Science

6. Beijing 100083

7. Key Laboratory of Fluid Interaction with Material

Abstract

The spectral data of calibration samples at room temperature and the spectral data of uncalibrated samples at high temperature are together transferred into a Hilbert space where an LIBS regression model is established.

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/2020/JA/D0JA00334D

Reference28 articles.

1. Rapid Identification of Genetically Modified Maize Using Laser-Induced Breakdown Spectroscopy

2. Detection of interstitial oxygen contents in Czochralski grown silicon crystals using internal calibration in laser-induced breakdown spectroscopy (LIBS)

3. The online detection of carbon isotopes by laser-induced breakdown spectroscopy

4. Quantitative analysis of Pb in soil samples by laser-induced breakdown spectroscopy with a simplified standard addition method

5. Infrared laser ablation and atomic emission spectrometry of stainless steel at high temperatures

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