A data selection method for matrix effects and uncertainty reduction for laser-induced breakdown spectroscopy

Severe matrix effects and high signal uncertainty are two key bottlenecks for the quantitative performance and wide applications of laser-induced breakdown spectroscopy (LIBS). Based on the understanding that the superposition of both matrix effects and signal uncertainty directly affects plasma parameters and further influences spectral intensity and LIBS quantification performance, a data selection method based on plasma temperature matching (DSPTM) was proposed to reduce both matrix effects and signal uncertainty. By selecting spectra with smaller plasma temperature differences for all samples, the proposed method was able to build up the quantification model to rely more on spectra with smaller matrix effects and signal uncertainty, therefore improving final quantification performance. When applied to quantitative analysis of the zinc content in brass alloys, it was found that both accuracy and precision were improved using either a univariate model or multiple linear regression (MLR). More specifically, for the univariate model, the root-mean-square error of prediction (RMSEP), the determination coefficients (R²) and relative standard derivation (RSD) were improved from 3.30%, 0.864 and 18.8% to 1.06%, 0.986 and 13.5%, respectively; while for MLR, RMSEP, R² and RSD were improved from 3.22%, 0.871 and 26.2% to 1.07%, 0.986 and 17.4%, respectively. These results prove that DSPTM can be used as an effective method to reduce matrix effects and improve repeatability by selecting reliable data.