Energy-dispersive X-ray fluorescence spectrometry (EDXRF) is a rapid and inexpensive method for soil analysis; however, analytical results are influenced by particle size effects and spectral interferences. The objective of this study was to optimise sample preparation and calibrations to improve the accuracy of EDXRF for soil tests. Methods of sample preparation were compared by calculating the recoveries of 13 elements in four International Soil-Analytical Exchange (ISE) standards prepared as loose powder (LP), pressed pellet (PP), and pressed pellet with wax binder (PPB). A matching library (ML) was created and evaluated against the fundamental parameters (FP) calibration using 20 ISE standards. Additionally, EDXRF results of 41 tillage soils were compared with Inductively coupled plasma optical emission spectrometry (ICP-OES) results. The PPB had most recoveries within the acceptable range of 80–120%; conversely, PP yielded the poorest element recoveries. For the calibration, the ML provided better recoveries of Ni, Pb, Cu, Mg, S, P, and Cr; however, for Zn, and Mn, it had the opposite effect. Furthermore, EDXRF results compared with ICP-OES separated by soil texture class for Al, K, Mn, and Fe. In conclusion, the EDXRF is suitable for quantifying both trace elements and macronutrients in contaminated soils and has the potential to provide screening or prediction of soil texture in agriculture.

Purpose: Information about particle size distribution (PSD) and soil texture is essential for understanding soil drainage, porosity, nutrient availability, and trafficability. The sieve-pipette/gravimetric method traditionally used for particle size analysis is labour-intensive and resource-intensive. X-ray fluorescence (XRF) spectrometry may provide a rapid alternative. The study’s aim was to examine the use of XRF for rapid determination of PSD in Irish soils. Methods: Soils (n = 355) from existing archives in Ireland were analysed with a benchtop energy-dispersive XRF (EDXRF). Correlation and regression analyses were determined to compare Rb, Fe, Al, and Si concentrations to % clay, % silt, and % sand. Also, linear regression models were developed to compare % clay, % sand, and % silt measured by the gravimetric method to values predicted by EDXRF. Results: The relationship between element concentration and PSD was dependent on parent material. Rb, Al, and Fe showed a significant (p < 0.05) correlation (r > 0.50) with % clay and % sand in soils derived from limestone and siliceous stone parent materials. Rb was the best predictor for % clay (R² = 0.49, RMSE = 10.20) in soils derived from limestone and siliceous stone-derived soils. Conclusion: Geochemistry and clay mineralogy of the soils’ parent material strongly influenced the EDXRF’s ability to predict particle size. The EDXRF could predict % clay in soils from parent materials which weather easily, but the opposite was true for soils with parent material recalcitrant to weathering. In conclusion, this study has shown that the EDXRF can screen % clay in soils derived from limestone and siliceous stone parent materials.

X-ray fluorescence spectrometry (XRF) is a rapid and inexpensive method for soil analysis. Although, not as precise and accurate as mineral acid digestion-Inductively Coupled Plasma spectrometry for soil testing at present, the XRF method has the potential to be optimised. The objective of this study was to reduce moisture effects, particle size effects and spectra interferences on trace element and macronutrient analyses by improving soil sample preparation and XRF calibration, using bench top Energy-dispersive X-ray Fluorescence Spectrometry (EDXRF). The soil particle size effects study involved samples prepared as pressed powders, pressed pellets, and pressed pellets with a wax binder. After which, the recoveries of Al, Cr, Ni, Mn, Pb, Ca, Fe, K, Mg, P, S and Zn were evaluated as a measure of accuracy and precision. The XRF was calibrated with the fundamental parameters (FP) and matching library (FPML) methods to reduce spectral interferences and validated with certified reference materials. In addition, both XRF methods (FP and FPML) were compared with aqua regia digestion (acid digestion) -Inductively Coupled Plasma-Optic Emission Spectrometry (ICP-OES) using concordance correlation coefficient (CCC), whereby a value of 1 indicated good agreement between methods. There were significant differences (p0.80) between both XRF methods and ICP-OES for all elements except Al, P, Cu, K, S and Cr (CCC<0.60). Aqua regia digestion underestimated the total concentration of Al and K, thus, the XRF had better accuracy for predicting these elements. However, for Cr, S, Cu and P, the XRF overestimated the concentration of these elements in soil. This study has shown that the XRF is as accurate as the ICP-spectrometry for most elements when properly calibrated with the advantage of having a shorter sample preparation. Thus, environmental analysts can monitor soils frequently using the XRF without losing analytical results especially with Al and K and also incurring substantial costs in analyses as with traditional methods. Keywords: soil; elemental analysis; X-Ray Fluorescence; spectrometry; calibration 

Soil texture is an essential factor for effective land management in agricultural production. Knowledge of soil texture and particle size at field scale can aid with on-going soil management decisions. Standard soil physical and gravimetric methods for particle size analysis are time-consuming and X-ray fluorescence spectrometry (XRF) provides a rapid and cost-effective alternative. The objective of this study was to explore the use of XRF as a predictor for particle size. An extensive archive of Irish soils with particle size and soil texture data was used to select samples for XRF analysis. Regression and correlation analyses on XRF determined results showed that the relationship between Rb and % clay varied with soil type and was dependent on the parent material. There was a strong relationship (R > 0.62, R2>0.30, p<0.05) between Rb and clay for soils originating from bedrock such as limestones and slate. Contrastingly, no significant relationship (R<0.03, R2=0.00, p>0.05) exists between Rb and % clay for soils originating from granite and gneiss. Furthermore, there was a significant negative correlation (p<0.05) between Rb and % sand. The XRF is a useful technique for rough screening of particle size distribution in soils originating from certain parent materials. Thus, this may contribute to the rapid prediction of soil texture based on knowledge of the particle size distribution. 

Deficiencies of essential and excesses of toxic elements contribute to substantial, yet frequently under‑recognized, global disease burden. Energy Dispersive (ED) X-ray fluorescence (XRF) spectroscopy is widely used for rapid, multi-elemental analysis. However, different instruments and XRF-system configurations vary in performance, constrained by sensitivity, matrix effects and spectral interferences. Presented here is a novel analytics solution. Integrating across common XRF platforms, sequences of systematic model building, and validation were performed with diverse machine-learning algorithms. A total of 85 certified reference materials, including 20 soils and 65 food and biological samples, were used to provide a comparable validation framework. Matrix-specific limits of detection were calculated, and platform performance was assessed. Hybrid calibration strategies using linear, partial least squares, and random forest enhanced XRF models, were developed to correct systematic biases. Unlike approaches based on direct prediction from high-dimensional spectra, this framework uses XRF-derived concentrations as inputs and corrects residual deviations. Machine learning-assisted calibration improved measurement accuracy, particularly for handheld XRF. However, no single correction model was universally applicable. Performance varied by element, matrix, and instrument. These results show that hybrid calibration improves the reliability of multi-elemental XRF analysis and supports its application in environmental and food system monitoring.