Single-Slice XRF Mapping of Light Elements in Frozen-Hydrated Allium schoenoprasum via a Self-Absorption-Corrected Hyperspectral Tomographic Reconstruction Approach.

Abstract

3D and 2D-cross-sectional X-ray fluorescence analysis of biological material is a powerful tool to image the distribution of elements and to understand and quantify metal homeostasis and the distribution of anthropogenic metals and nanoparticles with minimal preparation artifacts. Using tomograms recorded on cryogenically prepared leaves of Allium schoenoprasum, the cross-sectional distribution of physiologically relevant elements like calcium, potassium, manganese, and zinc could be tomographically reconstructed by peak fitting followed by a conventional maximum-likelihood algorithm with self-absorption correction to reveal the quantitative cross-sectional element distribution. If light elements such as S and P are located deep in the sample compared to the escape depth of their characteristic X-ray fluorescence lines, the quantitative reconstruction becomes inaccurate. As a consequence, noise is amplified to a magnitude where it might be misinterpreted as actual concentration. We show that a tomographic MCA hyperspectral reconstruction in combination with a self-absorption correction allows for fitting of the XRF spectra directly in real space, which significantly improves the qualitative and quantitative analysis of the light elements compared to the conventional method as noise and artifacts in the tomographic reconstruction are reduced. This reconstruction approach can substantially improve the quantitative analysis of trace elements as it allows the fitting of summed voxel spectra in anatomical regions of interest. The presented method can be applied to XRF 2D single-slice tomography data and 3D tomograms and is particularly relevant for, but not limited to, biological material in order to help retrieve self-absorption corrected quantitative reconstructions of the spatial distribution of light elements and ultra-trace-elements.