Portable X-ray fluorescence (pXRF) sensors allow one to collect digital data in a practical and environmentally friendly way, as a complementary method to traditional laboratory analyses. This work aimed to assess the performance of a pXRF sensor to predict exchangeable nutrients in soil samples by using two contrasting strategies of sample preparation: pressed pellets and loose powder (<2 mm). Pellets were prepared using soil and a cellulose binder at 10% w w−1 followed by grinding for 20 min. Sample homogeneity was probed by X-ray fluorescence microanalysis. Exchangeable nutrients were assessed by pXRF furnished with a Rh X-ray tube and silicon drift detector. The calibration models were obtained using 58 soil samples and leave-one-out cross-validation. The predictive capabilities of the models were appropriate for both exchangeable K (ex-K) and Ca (ex-Ca) determinations with R2 ≥ 0.76 and RPIQ > 2.5. Although XRF analysis of pressed pellets allowed a slight gain in performance over loose powder samples for the prediction of ex-K and ex-Ca, satisfactory performances were also obtained with loose powders, which require minimal sample preparation. The prediction models with local samples showed promising results and encourage more detailed investigations for the application of pXRF in tropical soils.

The successful use of energy-dispersive X-ray fluorescence (ED-XRF) sensors for soil analysis requires the selection of an optimal procedure of data acquisition and a simple modelling approach. This work aimed at assessing the performance of a portable XRF (XRF) sensor set up with two different X-ray tube configurations (combinations of voltage and current) to predict nine key soil fertility attributes: (clay, organic matter (OM), cation exchange capacity (CEC), pH, base saturation (V), and extractable nutrients (P, K, Ca, and Mg). An XRF, operated at a voltage of 15 kV (and current of 23 μA) and 35 kV (and current of 7 μA), was used for analyzing 102 soil samples collected from two agricultural fields in Brazil. Two different XRF data analysis scenarios were used to build the predictive models: (i) 10 emission lines of 15 keV spectra (EL-15), and (ii) 12 emission lines of 35 keV spectra (EL-35). Multiple linear regressions (MLR) were used for model calibration, and the models’ prediction performance was evaluated using different figures of merit. The results show that although X-ray tube configuration affected the intensity of the emission lines of the different elements detected, it did not influence the prediction accuracy of the studied key fertility attributes, suggesting that both X-ray tube configurations tested can be used for future analyses. Satisfactory predictions with residual prediction deviation (RPD) ≥ 1.54 and coefficient of determination (R2) ≥ 0.61 were obtained for eight out of the ten studied soil fertility attributes (clay, OM, CEC, V, and extractable K, Ca, and Mg). In addition, simple MLR models with a limited number of emission lines was effective for practical soil analysis of the key soil fertility attributes (except pH and extractable P) using XRF. The simple and transparent methodology suggested also enables future researches that seek to optimize the XRF scanning time in order to speed up the XRF analysis in soil samples.

μ-EDXRF is a strong analytical tool enabling the simultaneous mapping of As and P in the As-hyperaccumulator fern Pityrogramma calomelanos.

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).

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