Université Paris-Saclay, CNRS, Ministère de la Culture, UVSQ, IPANEMA, F-91192 Saint-Aubin, France.
Stanford Radiation Lightsource (SSRL), SLAC National Accelerator Laboratory, Menlo Park, CA, USA.
J Synchrotron Radiat. 2020 Jul 1;27(Pt 4):1049-1058. doi: 10.1107/S1600577520005822. Epub 2020 Jun 16.
One of the greatest benefits of synchrotron radiation is the ability to perform chemical speciation analysis through X-ray absorption spectroscopies (XAS). XAS imaging of large sample areas can be performed with either full-field or raster-scanning modalities. A common practice to reduce acquisition time while decreasing dose and/or increasing spatial resolution is to compare X-ray fluorescence images collected at a few diagnostic energies. Several authors have used different multivariate data processing strategies to establish speciation maps. In this manuscript, the theoretical aspects and assumptions that are often made in the analysis of these datasets are focused on. A robust framework is developed to perform speciation mapping in large bulk samples at high spatial resolution by comparison with known references. Two fully operational software implementations are provided: a user-friendly implementation within the MicroAnalysis Toolkit software, and a dedicated script developed under the R environment. The procedure is exemplified through the study of a cross section of a typical fossil specimen. The algorithm provides accurate speciation and concentration mapping while decreasing the data collection time by typically two or three orders of magnitude compared with the collection of whole spectra at each pixel. Whereas acquisition of spectral datacubes on large areas leads to very high irradiation times and doses, which can considerably lengthen experiments and generate significant alteration of radiation-sensitive materials, this sparse excitation energy procedure brings the total irradiation dose greatly below radiation damage thresholds identified in previous studies. This approach is particularly adapted to the chemical study of heterogeneous radiation-sensitive samples encountered in environmental, material, and life sciences.
同步辐射的最大优势之一是能够通过 X 射线吸收光谱学（XAS）进行化学形态分析。可以使用全视场或光栅扫描模式对大样品区域进行 XAS 成像。为了减少剂量和/或提高空间分辨率，同时缩短采集时间，可以比较少数几个诊断能量下采集的 X 射线荧光图像。一些作者已经使用不同的多元数据分析策略来建立形态分布图谱。本文重点关注分析这些数据集时经常涉及的理论方面和假设。提出了一个稳健的框架，通过与已知参考进行比较，在高空间分辨率下对大体积样品进行形态映射。提供了两种完全可操作的软件实现：一种是在 MicroAnalysis Toolkit 软件中实现的用户友好型实现，另一种是在 R 环境下开发的专用脚本。通过对典型化石标本的横截面进行研究，说明了该方法的应用。该算法在减少数据采集时间的同时，提供了准确的形态和浓度映射，与在每个像素处采集全谱相比，通常减少了两个或三个数量级的数据采集时间。虽然在大面积上采集光谱数据立方体会导致非常高的辐照时间和剂量，这可能会显著延长实验并导致对辐射敏感材料的显著改变，但这种稀疏激发能量的方法将总辐照剂量大大降低到了以前研究中确定的辐射损伤阈值以下。这种方法特别适用于环境、材料和生命科学中遇到的不均匀辐射敏感样品的化学研究。
