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Evaluation of quantitative synchrotron radiation micro‐X‐ray fluorescence in rice grain
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Evaluation of quantitative synchrotron radiation micro‐X‐ray fluorescence in rice grain
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Evaluation of quantitative synchrotron radiation micro‐X‐ray fluorescence in rice grain
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Journal Article
Evaluation of quantitative synchrotron radiation micro‐X‐ray fluorescence in rice grain
2023
Overview
Concentrations of nutrients and contaminants in rice grain affect human health, specifically through the localization and chemical form of elements. Methods to spatially quantify the concentration and speciation of elements are needed to protect human health and characterize elemental homeostasis in plants. Here, an evaluation was carried out using quantitative synchrotron radiation microprobe X‐ray fluorescence (SR‐µXRF) imaging by comparing average rice grain concentrations of As, Cu, K, Mn, P, S and Zn measured with rice grain concentrations from acid digestion and ICP‐MS analysis for 50 grain samples. Better agreement was found between the two methods for high‐Z elements. Regression fits between the two methods allowed quantitative concentration maps of the measured elements. These maps revealed that most elements were concentrated in the bran, although S and Zn permeated into the endosperm. Arsenic was highest in the ovular vascular trace (OVT), with concentrations approaching 100 mg kg−1 in the OVT of a grain from a rice plant grown in As‐contaminated soil. Quantitative SR‐µXRF is a useful approach for comparison across multiple studies but requires careful consideration of sample preparation and beamline characteristics. This work demonstrates which elements are the most easily quantifiable by synchrotron radiation for microprobe X‐ray fluorescence (SR‐µXRF) imaging in order to understand elemental distributions in plant tissues, with a focus on metals and metalloids in rice grain. This work provides SR‐µXRF users with data to understand which elements can be reliably quantified and guidance on how to consider the limitations of the technique to most effectively interpret such data.
