A Calibration-Free Laser-Induced Breakdown Spectroscopy (CF-LIBS) Quantitative Analysis Method Based on the Auto-Selection of an Internal Reference Line and Optimized Estimation of Plasma Temperature. Issue 1 (January 2018)

• Record Type: Journal Article
• Authors: Yang, Jianhong; Li, Xiaomeng; Xu, Jinwu; Ma, Xianghong
• Is Part Of: Applied spectroscopy. Volume 72:Issue 1(2018)
• Page Start: 129
• Page End: 140
• Publication Date: 2018-01
• Subjects: Calibration-free laser-induced breakdown spectroscopy -- CF-LIBS -- self-absorption correction -- internal reference line auto-selection -- plasma temperature estimation; Spectrum analysis -- Periodicals; 543.505
• DOI: 10.1177/0003702817734293
• Languages: English
• ISSNs: 0003-7028

Abstract

The quantitative analysis accuracy of calibration-free laser-induced breakdown spectroscopy (CF-LIBS) is severely affected by the self-absorption effect and estimation of plasma temperature. Herein, a CF-LIBS quantitative analysis method based on the auto-selection of internal reference line and the optimized estimation of plasma temperature is proposed. The internal reference line of each species is automatically selected from analytical lines by a programmable procedure through easily accessible parameters. Furthermore, the self-absorption effect of the internal reference line is considered during the correction procedure. To improve the analysis accuracy of CF-LIBS, the particle swarm optimization (PSO) algorithm is introduced to estimate the plasma temperature based on the calculation results from the Boltzmann plot. Thereafter, the species concentrations of a sample can be calculated according to the classical CF-LIBS method. A total of 15 certified alloy steel standard samples of known compositions and elemental weight percentages were used in the experiment. Using the proposed method, the average relative errors of Cr, Ni, and Fe calculated concentrations were 4.40%, 6.81%, and 2.29%, respectively. The quantitative results demonstrated an improvement compared with the classical CF-LIBS method and the promising potential of in situ and real-time application.