Multiple Liner Regression for Improving the Accuracy of Laser-Induced Breakdown Spectroscopy Assisted With Laser-Induced Fluorescence (LIBS-LIF)
WU Jie1, LI Chuang-kai1, CHEN Wen-jun1, HUANG Yan-xin1, ZHAO Nan1, LI Jia-ming1, 2*, YANG Huan3, LI Xiang-you4, LÜ Qi-tao3,5, ZHANG Qing-mao1,2,5
1. Guangdong Provinical Key Laboratory of Nanophotonic Functional Materials and Devices, School of Information and Optoelectronic Science and Engineering, South China Normal University, Guangzhou 510006, China
2. Province-Ministry Co-construction State Key Laboratory of Optic Information Physics and Technologies, South China Normal University, Guangzhou 510006, China
3. Sino-German College of Intelligent Manufacturing, Shenzhen Technology University, Shenzhen 518118, China
4. Wuhan National Laboratory for Optoelectronics (WNLO), Huazhong University of Science and Technology, Wuhan 430074, China
5. Guangdong Provincial Key Laboratory of Industrial Ultrashort Pulse Laser Technology, Shenzhen 518055, China
Abstract：Elemental analysis is an essential requirement in the metallurgical industry, nuclear industry, pollution detection and environmental monitoring. As a new type of atomic spectrum analysis technology, LIBS has been widely concerned because of its real-time, fast, almost non-destructive and multi-element simultaneous analysis. However, its poor analytical sensitivity has restricted the development of this technology. LIBS-LIF can improve the sensitivity of analysis and efficiently detect the element types of samples through laser resonance excitation. The spectrometer can collect spectral information and a model can be established to predict the concentration of unknown samples. However, when the characteristic spectral lines of the matrix atom and the target atom are very close, the matrix spectral lines will be affected, and the unary calibration accuracy will decrease. In this paper, linear models of Ni and Cr elements in steel were established using linear fitting with one variable and linear fitting with multiple variables. Firstly, the peak spectral line in the sample spectral map is selected to find whether it is the characteristic spectral line corresponding to the element to be measured or the collective element. After selecting suitable characteristic spectral lines, the spectral intensities of multiple spectral lines and the concentrations of the elements to be measured in the sample were used as a multivariate linear fitting model, and the fitting coefficients corresponding to each spectral line were ranked from highest to lowest, and the contribution of the spectral intensities corresponding to each characteristic spectral line in the multivariate linear fitting model to the concentration prediction was taken as the criterion from highest to lowest, and the fitting dimension was increased continuously. The mean relative errors of the regression models for Ni and Cr elemental content were reduced from 38% to about 10% and 55% to within 25%, respectively, and the root mean square error values of the cross-validation of the linear regression models for Ni and Cr elemental content were reduced from 3.4% to 2% and 2.5%, respectively, with the increase of dimensionality. and 2.5% to 1.5% for Ni and Cr, respectively. In this paper, the method of selecting multiple spectral lines to establish a multiple linear regression model is relatively effective in reducing the influence of excitation interference, and it puts forward a feasible scheme for promoting the practical application of laser-induced fluorescence assisted laser-induced laser spectroscopy technology in element analysis.
Key words：Laser-induced breakdown spectroscopy; Laser-induced Fluorescence; Multiple linear regression
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