X-ray diffraction tomography is a well-developed technique to study the structure of heterogeneous materials which makes it a tool of choice for the non-destructive investigation of cultural heritage microsamples. Characterizing such complex materials with a high enough spatial resolution requires acquiring large amounts of diffraction images, followed by a complex sequence of data management to localize the crystalline phases in the sample. Here we propose to use multivariate analysis in order to automatically decompose the data in a small set of components, each representing the diffraction pattern of one or a small number of phases. This makes phase identification and quantification of each component much more efficient and leads to a quantitative knowledge of the phase content in each voxel of the tomographic reconstruction. We show that non-negative matrix factorization is very efficient for this purpose, with a computing time well compatible with in-line data analysis in order to assess the quality of measurements during experiments at synchrotron beamlines. Here, we apply the method to the investigation of microsamples from medieval applied brocade decors selected from a wooden statue, and the results are validated a posteriori by comparison with ex situ destructive techniques.

中文翻译：

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将多元分析应用于 X 射线衍射计算机断层扫描：中世纪应用锦缎的研究

X 射线衍射断层扫描是研究异质材料结构的成熟技术，使其成为文化遗产微样本无损研究的首选工具。以足够高的空间分辨率表征此类复杂材料需要获取大量衍射图像，然后进行复杂的数据管理序列以定位样品中的结晶相。在这里，我们建议使用多变量分析来自动分解一小组组件中的数据，每个组件代表一个或少数几个阶段的衍射图案。这使得每个组件的相位识别和量化更加有效，并导致对断层扫描重建的每个体素中的相位含量的定量了解。我们表明，非负矩阵分解为此目的非常有效，其计算时间与在线数据分析非常兼容，以便在同步加速器光束线实验期间评估测量质量。在这里，我们将该方法应用于从木制雕像中选择的中世纪应用锦缎装饰的微量样品的调查，并验证了结果与易地破坏性技术相比的后验。