Determination of Lead and Arsenic in Soil Samples by X Fluorescence Spectrum Combined With CARS Variables Screening Method

As a quantitative analysis technique based on stoichiometry, X-ray fluorescence spectroscopy is very important to the prediction accuracy of the results. The competitive adaptive reweighted algorithm (CARS) adopted adaptive reweighted sampling technology and used interactive verification to select the lowest value square error (RMSECV) by interactive verification to find out the optimal combination of variables. To further improving the interpretation and prediction ability of PLS models, the competitive adaptive reweighted algorithm (CARS) was combined with X-ray fluorescence spectroscopy. A partial least square (PLS) model was established after screening the characteristic wavelength variables of lead and arsenic in the soil. Firstly, the CARS algorithm screened the wavelength variables closely related to lead content. When the sampling times were 26 times, 60 effective wavelength points were selected, and the wavelength variables closely related to arsenic content were screened. When the sampling times were 34 times, 19 effective wavelength points were selected. Then used the PLS method to establish the quantitative analysis model of lead and arsenic content in soil and compared it with the PLS model established by continuous projection algorithm (SPA) and Monte Carlo method. The results showed that the prediction sets Determination Coefficient (R-2), Root Mean Square Error of Cross-Validation (RMSECV), Root Mean Square Error of Prediction (RMSEP) and Relative Prediction Deviation (RPD) of the lead CARS-PLS model were 0. 995 5, 2. 598 6, 3. 228 and 9. 401 1, respectively. Moreover, the prediction sets R2, RMSECV, RMSEP and RPD of arsenic CARS-PLS models were 0. 999, 3. 013 2, 2. 737 1 and 8. 211 6, respectively. The CARS-PLS model performance of the two elements is better than that of full-band PLS, SPA-PLS and MC-UVE-PLS model. The CARS-PLS algorithm based on the X fluorescence spectrum can effectively screen the characteristic wavelength, simplify the complexity of modeling, and improve the accuracy and robustness of the model.