Combination of the internal standard and dominant factor PLS for improving long-term stability of LIBS measurements

Abstract

Improving long-term stability is an important issue for large-scale applications of laser-induced breakdown spectroscopy (LIBS). Unlike laboratory instruments, many applications of LIBS instruments are in harsh outdoor environments, where instrument drift can lead to deterioration of long-term stability, hindering the use of LIBS technology in applications with high-precision requirements. In this work, based on the designed LIBS sensor for molten steel, we analyzed the spectral drift problem of different day measurements, on the basis of which we proposed a drift correction method combining the internal standard and the dominant factor partial least squares (PLS) regression. In this method, spectra are first preprocessed to build an internal standard model of the elemental concentration ratios to spectral line intensity ratios. Then the PLS regression is utilized to construct a corrected model between the spectral intensity and the drift value of the intensity ratio. Finally, elemental concentration predictions are made by using the established internal standard model with modified spectral line intensity ratios. The method was tested on low alloy steel samples. In the experiment, the detection spectra were recorded for 9 days for quantitative analysis and drift correction of the major elements C, Si, Cr, Ni, Cu, and Mn in alloy steels. Compared with the uncalibrated internal standard method, for the prediction of unknown samples over a long period of time, the RMSE values of C, Si, Cr, Ni, Cu, and Mn decreased by 48.74%, 50.00%, 73.30%, 72.15%, 72.57%, and 18.23%, respectively, and the RSD decreased by 27.71%, 42.97%, 35.17%, 38.95%, 55.58%, and 23.40%, respectively. Furthermore, several typical drift correction methods were also studied for comparison, and the proposed method achieved the best results for different test sets.