laser-induced breakdown spectroscopy; soil; manganese;QUANTITATIVE-ANALYSIS; LIBS; SAMPLES; ALLOY

Laser-induced breakdown spectroscopy (LIBS) has been applied to measure spectral characteristics and to perform quantitative analysis of the concentration of manganese in soil, an issue of great concern for precision agriculture. For the analysis, soil samples were compressed into pellets and a pulsed Nd:YAG laser was employed to produce the plasma in air at atmospheric pressure. Using this approach, we analyzed the time evolution of spectral characteristics and their dependence on the laser pulse energy. A calibration curve was constructed using reference sandy soil samples collected from a farm. An internal standard curve was used to improve the accuracy of the LIBS metrology for soil analyses. The results of this analysis demonstrated the usefulness of this method for analyzing the concentration of manganese in soil.

laser-induced breakdown spectroscopy; soil; magnesium; nonlinear multivariate calibration;RARE-EARTH-ELEMENTS; SAMPLES; LIBS; QUANTIFICATION; DIAGNOSIS; METALS

Laser-induced breakdown spectroscopy (LIBS) coupled with the nonlinear multivariate regression method was applied to analyze magnesium (Mg) contents in soil. The plasma was generated using a 100 mJ Nd:YAG pulsed laser, and the spectra were acquired using a multi-channel spectrometer integrated with a CCD detector. The line at 383.8 nm was selected as the analysis line for Mg. The calibration model between the intensity of characteristic line and the concentration of Mg was constructed. The traditional calibration curve showed that the concentration of Mg was not only related to the line intensity of itself, but also to other elements in soil. The intensity of characteristic lines for Mg (Mg I 383.8 nm), manganese (Mn) (Mn I 403.1 nm), and iron (Fe) (Fe I 407.2 nm) were used as input data for nonlinear multivariate calculation. According to the results of nonlinear regression, the ternary nonlinear regression was the most appropriate of the studied models. A good agreement was observed between the actual concentration provided by inductively coupled plasma mass spectrometry (ICP-MS) and the predicted value obtained using the nonlinear multivariate regression model. The correlation coefficient between predicted concentration and the measured value was 0.987, while the root mean square error of calibration (RMSEC) and root mean square error of prediction (RMSEP) were reduced to 0.017% and 0.014%, respectively. The ratio of the standard deviation of the validation to the RMSEP increased to 8.79, and the relative error was below 1.21% for nine validation samples. This indicated that the multivariate model can obtain better predicted accuracy than the calibration curve. These results also suggest that the LIBS technique is a powerful tool for analyzing the micro-nutrient elements in soil by selecting calibration and validation samples with similar matrix composition.