Quantitative analysis of pH value in soil using laser-induced breakdown spectroscopy coupled with a multivariate regression method.

Abstract

The quantitative analyses of pH value in soil have been performed using laser-induced breakdown spectroscopy (LIBS) technology. The aim of this work was to obtain a reliable and accurate method for rapid detection of pH value in soil. Seventy-four samples were used as a calibration set, and 24 samples were used as a prediction set. To eliminate the matrix effect, the multivariate models of partial least-squares regression (PLSR) and least-squares support vector regression (LS-SVR) were used to construct the models. The intensities of nine emission lines of C, Ca, Na, O, H, Mg, Al, and Fe elements were used to fit the models. For the PLSR model, the correlation coefficient was 0.897 and 0.906 for the calibration and prediction set, respectively. Furthermore, the analysis accuracy was improved effectively by the LS-SVR method, and the correlation coefficients for calibration and prediction set were improved to 0.991 and 0.987. The prediction mean absolute error was pH 0.1 units, and the root mean square error of the prediction was only 0.079. The results indicated that the LIBS technique coupled with LS-SVR could be a reliable and accurate method for determining pH value in soil.