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Updated: Oct 28, 2026

Quantifying X-Ray Fluorescence Data Using MAPS
Published on: February 17, 2018
Advancing Quantitative XRF Analysis of Plant Matrices Through Validation and Extension of Element-Specific Correction
Riccardo Fedeli1, Konstantin Vergel2, Vladimir Galustov2
1BioAgry Lab, University of Siena, Via Pier Andrea Mattioli 4, 53100 Siena, Italy.
Abstract:
X-ray fluorescence (XRF) provides a rapid, non-destructive approach for the multi-element analysis of plant materials, although its quantitative application is limited by matrix-dependent effects that can cause substantial deviations from real elemental concentrations. To compensate for systematic XRF overestimation in plant matrices, element-specific correction factors (CFs) were derived from the ratio between certified elemental concentrations and XRF-measured concentrations in plant certified reference materials (CRMs). Here, the applicability of these CRM-derived CFs to real plant samples was independently validated by comparing corrected XRF results with Instrumental Neutron Activation Analysis (INAA) measurements for 165 moss samples. Application of the CFs markedly improved quantitative agreement, yielding median recoveries close to unity (0.79-1.16) for Zn, K, Ca, Mn, Sr, Rb, Cl, and Fe. The XRF-INAA comparison also enabled the evaluation of additional elements for which CFs had not been established using CRMs. Strong correlations were obtained for Ti and Ni in both XRF analytical modes (ρ = 0.70-0.86), as well as for As in Soil mode (ρ = 0.73) and Al in Geochem mode (ρ = 0.79), allowing new element-specific CFs to be proposed. Conversely, Ba showed mode-dependent performance, while Cr and V exhibited weak or negligible correlations with INAA, preventing reliable correction. Overall, these findings demonstrate that, for the pXRF instrument and analytical configurations investigated here, CRM-derived CFs can be successfully applied to field-collected moss samples, expand the range of elements that can be reliably quantified under these measurement conditions, and strengthen the potential of pXRF for routine multi-element analysis of plant matrices.
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