Multivariate analysis of Laser-Induced breakdown spectroscopy spectra of soil samples

Laser-induced breakdown spectroscopy (LIBS) is a rapid quantitative analytical technique that can be used to determine the elemental composition of numerous sample matrices, and it has been successfully applied in many types of samples. However, for chemically and physically complex soil samples, its quantitative analytical ability is controversial. Multivariate analytical techniques have great potential for analyzing the complex LIBS spectra. To demonstrate the feasibility of LIBS as an alternative technique to quantitatively analyze soil samples, the univariate and the partial least square (PLS) techniques are used to analyze the LIBS spectra of 12 soil samples and to build calibration models predicting Cu and Zn concentrations. The results show that PLS can significantly improve the analytical results compared with the univariate technique. The normalized root mean square error (NRMSE) and r of the univariate models are 16.60% and 0.71 in calibration and 18.80% and 0.62 in prediction for Cu and 18.97% and 0.62 in calibration and 22.81% and 0.45 in prediction for Zn. For the PLS models using the spectral range 300 to 350 nm, the NRMSE and r² are 1.94% and 0.99 for both Cu and Zn in calibration and 7.90% and 0.94 for Cu and 8.14% and 0.94 for Zn in prediction, respectively. Compared with the univariate technique, PLS improves the NRMSE 87.53% and 87.78% in calibration and 44.47% and 53.44% in prediction for Cu and Zn, respectively. The results indicate that PLS can improve the quantitative analytical ability of LIBS for soil sample analysis.