摘要
Portable X-ray fluorescence(pXRF) spectrometers can be used to determine the elemental composition easily, rapidly, and without using chemical reagents, which is very important for tropical regions due to the lack of detailed soil characterization data. Moreover,pXRF data can be used to predict the results of more expensive, time-consuming, and conventional laboratory analyses. This study sought to determine the elemental composition of various soil profiles using pXRF. Two operational modes(Trace Mode and General Mode) and two scanning time(30 and 60 s) were assessed to determine their effects on the correlation of pXRF dataset with respect to conventional inductively coupled plasma(ICP)-optical emission spectrometry analysis. This relationship has been reported in previous studies, however, few studies were performed on tropical soils, which are unique. Furthermore, such relationships establish the viability of developing prediction models directly from pXRF data. Linear regression was applied to develop calibration models for the prediction of ICP analysis results and exchangeable and available elemental contents based on pXRF data. High coefficients of determination(R^2) were obtained for Ca(0.87), Cu(0.90), Fe(0.95), Mn(0.85), Cr(0.95), V(0.72), and Ni(0.90), with adequate validation. Statistically significant results were not found for Al, K, Zn, Ti, and Zr. The models predicting the exchangeable Ca based on the total Ca from p XRF reached an R^2 of up to 0.85. Operational modes influenced the pXRF results. Our results illustrate that pXRF holds great promise for tropical soil characterization and the development of prediction models, justifying the need for larger-scale studies in tropical countries worldwide.
Portable X-ray fluorescence(pXRF) spectrometers can be used to determine the elemental composition easily, rapidly, and without using chemical reagents, which is very important for tropical regions due to the lack of detailed soil characterization data. Moreover,pXRF data can be used to predict the results of more expensive, time-consuming, and conventional laboratory analyses. This study sought to determine the elemental composition of various soil profiles using pXRF. Two operational modes(Trace Mode and General Mode) and two scanning time(30 and 60 s) were assessed to determine their effects on the correlation of pXRF dataset with respect to conventional inductively coupled plasma(ICP)-optical emission spectrometry analysis. This relationship has been reported in previous studies, however, few studies were performed on tropical soils, which are unique. Furthermore, such relationships establish the viability of developing prediction models directly from pXRF data. Linear regression was applied to develop calibration models for the prediction of ICP analysis results and exchangeable and available elemental contents based on pXRF data. High coefficients of determination(R^2) were obtained for Ca(0.87), Cu(0.90), Fe(0.95), Mn(0.85), Cr(0.95), V(0.72), and Ni(0.90), with adequate validation. Statistically significant results were not found for Al, K, Zn, Ti, and Zr. The models predicting the exchangeable Ca based on the total Ca from p XRF reached an R^2 of up to 0.85. Operational modes influenced the pXRF results. Our results illustrate that pXRF holds great promise for tropical soil characterization and the development of prediction models, justifying the need for larger-scale studies in tropical countries worldwide.
基金
the Brazilian funding agencies
National Council for Scientific and Technological Development(CNPq)
Coordination of Superior Level Staff Improvement(CAPES)
Foundation for Research of the State of Minas Gerais(FAPEMIG)
