Abstract:Portable X‐ray fluorescence (PXRF) spectrometry is a proximal sensing technique whereby low‐power X‐rays are used to make elemental determinations in soils. The technique is rapid, portable, and provides multi‐elemental analysis with results generally comparable to traditional laboratory‐based techniques. Elemental data from PXRF can then be either used directly for soil parameter assessment (e.g., total Ca, total Fe) or as a proxy for predicting other soil parameters of interest (e.g., soil cation‐exchange ca… Show more
“…The prediction performance of PLSR and MLR models calibrated with both XRF preprocessing scenarios (EL and ELC) were compared with models calibrated using the Geo Exploration package. The latter was calibrated for all the studied soil fertility attributes using MLR and for the contents of Al 2 O 3 , SiO 2 , K 2 O, Ca, Ti, Mn, Fe, Ni, and Cu as input X-variables, as suggested by Weindorf and Chakraborty [27]. The same above-mentioned calibration and validation datasets were used.…”
Section: Modelingmentioning
confidence: 99%
“…These packages also implement solutions to mitigate the matrix effect, facilitating the use of XRF sensors by nonexpert users [26]. However, preprogrammed measurement packages are associated with a pre-established scanning time (e.g., generally between: 60 and 90 s) [27], which is a constraint for the development of proximal soil sensing (PSS) applications that require rapid analysis (e.g., online analysis using mobile platforms). In this regard, new solutions for XRF data acquisition and analysis, avoiding the use of preprogrammed measurement packages and using the direct information of XRF spectra, should be developed to expand the use of XRF sensors in the PSS context.…”
The matrix effect is one of the challenges to be overcome for a successful analysis of soil samples using X-ray fluorescence (XRF) sensors. This work aimed at evaluation of a simple modeling approach consisted of Compton normalization (CN) and multivariate regressions (e.g., multiple linear regressions (MLR) and partial least squares regression (PLSR)) to overcome the soil matrix effect, and subsequently improve the prediction accuracy of key soil fertility attributes. A portable XRF was used for analyzing 102 soil samples collected from two agricultural fields with contrasting soil matrices. Using the intensity of emission lines as input, preprocessing methods included with and without the CN. Univariate regression models for the prediction of clay, cation exchange capacity (CEC), and exchangeable (ex-) K and Ca were compared with the corresponding MLR models to assess matrix effect mitigation. The MLR and PLSR models improved the prediction results of the univariate models for both preprocessing methods, proving to be promising strategies for mitigating the matrix effect. In turn, the CN also mitigated part of the matrix effect for ex-K, ex-Ca, and CEC predictions, by improving the predictive performance of these elements when used in univariate and multivariate models. The CN has not improved the prediction accuracy of clay. The prediction performances obtained using MLR and PLSR were comparable for all evaluated attributes. The combined use of CN with multivariate regressions (MLR or PLSR) achieved excellent prediction results for CEC (R2 = 0.87), ex-K (R2 ≥ 0.94), and ex-Ca (R2 ≥ 0.96), whereas clay predictions were comparable with and without CN (0.89 ≤ R2 ≤ 0.92). We suggest using multivariate regressions (MLR or PLSR) combined with the CN to remove the soil matrix effects and consequently result in optimal prediction results of the studied key soil fertility attributes. The prediction performance observed for this solution showed comparable results to the approach based on the preprogrammed measurement package tested (Geo Exploration package, Bruker AXS, Madison, WI, USA).
“…The prediction performance of PLSR and MLR models calibrated with both XRF preprocessing scenarios (EL and ELC) were compared with models calibrated using the Geo Exploration package. The latter was calibrated for all the studied soil fertility attributes using MLR and for the contents of Al 2 O 3 , SiO 2 , K 2 O, Ca, Ti, Mn, Fe, Ni, and Cu as input X-variables, as suggested by Weindorf and Chakraborty [27]. The same above-mentioned calibration and validation datasets were used.…”
Section: Modelingmentioning
confidence: 99%
“…These packages also implement solutions to mitigate the matrix effect, facilitating the use of XRF sensors by nonexpert users [26]. However, preprogrammed measurement packages are associated with a pre-established scanning time (e.g., generally between: 60 and 90 s) [27], which is a constraint for the development of proximal soil sensing (PSS) applications that require rapid analysis (e.g., online analysis using mobile platforms). In this regard, new solutions for XRF data acquisition and analysis, avoiding the use of preprogrammed measurement packages and using the direct information of XRF spectra, should be developed to expand the use of XRF sensors in the PSS context.…”
The matrix effect is one of the challenges to be overcome for a successful analysis of soil samples using X-ray fluorescence (XRF) sensors. This work aimed at evaluation of a simple modeling approach consisted of Compton normalization (CN) and multivariate regressions (e.g., multiple linear regressions (MLR) and partial least squares regression (PLSR)) to overcome the soil matrix effect, and subsequently improve the prediction accuracy of key soil fertility attributes. A portable XRF was used for analyzing 102 soil samples collected from two agricultural fields with contrasting soil matrices. Using the intensity of emission lines as input, preprocessing methods included with and without the CN. Univariate regression models for the prediction of clay, cation exchange capacity (CEC), and exchangeable (ex-) K and Ca were compared with the corresponding MLR models to assess matrix effect mitigation. The MLR and PLSR models improved the prediction results of the univariate models for both preprocessing methods, proving to be promising strategies for mitigating the matrix effect. In turn, the CN also mitigated part of the matrix effect for ex-K, ex-Ca, and CEC predictions, by improving the predictive performance of these elements when used in univariate and multivariate models. The CN has not improved the prediction accuracy of clay. The prediction performances obtained using MLR and PLSR were comparable for all evaluated attributes. The combined use of CN with multivariate regressions (MLR or PLSR) achieved excellent prediction results for CEC (R2 = 0.87), ex-K (R2 ≥ 0.94), and ex-Ca (R2 ≥ 0.96), whereas clay predictions were comparable with and without CN (0.89 ≤ R2 ≤ 0.92). We suggest using multivariate regressions (MLR or PLSR) combined with the CN to remove the soil matrix effects and consequently result in optimal prediction results of the studied key soil fertility attributes. The prediction performance observed for this solution showed comparable results to the approach based on the preprogrammed measurement package tested (Geo Exploration package, Bruker AXS, Madison, WI, USA).
“…Such results were expected, as pXRF is more sensitive for heavier elements due to its higher excitation energy. 39,40 Figure 2.Scatter plots of pXRF readings versus XRF-determined Fe or Si concentrations (a, b) display the results for Fe and Si, respectively, in Fe ore concentrate from China.…”
As a technique capable of rapid, nondestructive, and multi-elemental analysis, portable X-ray fluorescence (pXRF) has applications to mineral exploration, environmental evaluation, and archaeological analysis. However, few applications have been conducted in the smelting industry especially when analyzing the metal concentration in ore concentrate samples. This research analyzed the effectiveness of using pXRF in determining the metal concentration in Fe concentrate. For this proof of concept study, Fe ore samples dominated by Fe and Si were collected from the Northeastern University Mineral Processing Laboratory (Shenyang, China) and directly analyzed using pXRF, laboratory-based XRF, and titration methods. The compactness (density) of the ore concentrate was found to have very little effect on pXRF readings. The pXRF readings for Fe and Si were comparative to laboratory-based XRF results. Based on the strong correlations between the pXRF and XRF results (Fe: R2 > 0.99, Si: R2 > 0.96), linear calibrations were adopted to improve the accuracy of pXRF readings. Linear regression equations derived from the relations between XRF results and pXRF results of 21 Fe ore concentrate samples were used to calibrate the pXRF, and then validation was performed on five additional samples. Results from this preliminary study suggest that ordinary least squares (OLS) regression improves the accuracy dramatically, especially for Fe with relative errors (REs) decreasing to 0.03%–3.27% from 4.26%–8.32%. Consequently, pXRF shows strong promise for rapid, quantitative analysis of Fe concentration in Fe ore concentrate. Based on the results obtained in this study, a larger, more comprehensive study is warranted to confirm the results obtained.
“…The elemental analysis of both rocks was obtained using DP-6000 Delta Premium portable X-ray fluorescence (PXRF) spectrometer (Olympus, Waltham, MA, USA) according to Weindorf and Chakraborty (2016). The instrument features a Rh X-ray tube operated at 15-40 kV with quantification via ultra-high resolution (165 eV) silicon drift detector.…”
Section: General Characterizationmentioning
confidence: 99%
“…The analysis was firstly conducted in "Soil Mode" (three beams of 30 s each), and secondly in "Geochem Mode" (two beams of 30 s each). Therefore, the contents of Al, Si, P, Mg, Ca, and S were validated using "Geochem Mode" readings, and the remaining elements were validated using "Soil Mode" (Weindorf and Chakraborty, 2016).…”
The aim of this study was to evaluate the feasibility of using phosphate rock and dolostone as fertilizer and amendment, respectively, for application in tropical acid soils. The dissolution of different particle-size fractions by water and citric acid was studied. Laboratory column experiments were run following a completely randomized design, by using 0.063 -0.25, 0.25 -0.5, and 0.5 -2 mm particle-size fractions of both rocks. Each rock particle-size was subjected to exhaustive dissolution with distilled water, citric acid solution at pH 4, and citric acid solution at pH 2, with the following extraction times: 1, 3, 5, 7, 12, 24, 72, 144, 240, and 360 h. The dissolution of both rocks depended on particle-size, leaching solution and extraction time. The dissolution rate of rock-forming minerals augmented as the specific surface area increased, corresponding to a decrease in particlesize. In all cases, the kinetics of release was characterized by two phases: 1) a first stage of rapid release that lasted 24 h, which would ensure short-term nutrient release, and 2) a second stage of slow release (after 24 h), representing the long-term nutrient release efficiency. Both rocks are suitable as slow release fertilizers in strongly acid soils and would ensure the replenishment of P, Ca, and Mg. A combination of fine and medium particle-size fractions should be used to ensure high nutrient release efficiency. Much work has to be done to assess the overall impact of considerable amounts of fresh rocks in soils.
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