Abstract
In most cases, the analysis of small-angle and wide-angle X-ray scattering (SAXS and WAXS, respectively) requires a theoretical model to describe the sample's scattering, complicating the interpretation of the scattering resulting from complex heterogeneous samples. This is the reason why, in general, the analysis of a large number of scattering patterns, such as are generated by time-resolved and scanning methods, remains challenging. Here, a model-free classification method to separate SAXS/WAXS signals on the basis of their inflection points is introduced and demonstrated. This article focuses on the segmentation of scanning SAXS/WAXS maps for which each pixel corresponds to an azimuthally integrated scattering curve. In such a way, the sample composition distribution can be segmented through signal classification without applying a model or previous sample knowledge. Dimensionality reduction and clustering algorithms are employed to classify SAXS/WAXS signals according to their similarity. The number of clusters, i.e. the main sample regions detected by SAXS/WAXS signal similarity, is automatically estimated. From each cluster, a main representative SAXS/WAXS signal is extracted to uncover the spatial distribution of the mixtures of phases that form the sample. As examples of applications, a mudrock sample and two breast tissue lesions are segmented.
Highlights
The high flux of modern light sources allows small-angle X-ray scattering (SAXS) and wide-angle X-ray scattering (WAXS) measurements to proceed rapidly and to produce significant data sets, often with a continuous sampling rate in excess of 10 Hz
The input data were prepared from measured SAXS/WAXS signals (Fig. 3a) as an r  q matrix M1, where r is the number of IiðqÞ curves and q is the range of scattering vector moduli in IiðqÞ
We present a method to automatically classify scattering curves of SAXS/WAXS measurements according to feature extraction of their inflection points, such as the presence of Bragg peaks and slope variations
Summary
The high flux of modern light sources allows small-angle X-ray scattering (SAXS) and wide-angle X-ray scattering (WAXS) measurements to proceed rapidly and to produce significant data sets, often with a continuous sampling rate in excess of 10 Hz. The high flux of modern light sources allows small-angle X-ray scattering (SAXS) and wide-angle X-ray scattering (WAXS) measurements to proceed rapidly and to produce significant data sets, often with a continuous sampling rate in excess of 10 Hz From each such measurement, parameters characteristic of the sample, such as composition, homogeneity, and particle size and shape, can be extracted. Parameters characteristic of the sample, such as composition, homogeneity, and particle size and shape, can be extracted The interpretation of such high data volumes generated by scanning and timeresolved SAXS/WAXS methods is facilitated by statistical approaches.
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