论文标题
X射线吸收光谱数据自动脱胶的算法
An algorithm for the automatic deglitching of x-ray absorption spectroscopy data
论文作者
论文摘要
X射线吸收光谱（XAS）数据的分析通常涉及从获得的信号中清除伪影或故障，这是通常称为Deglitching的过程。故障是由单色晶体的特定方向或样品本身中的晶体散射而产生的。由于不能总是预测频谱中精确的能量或波长位置以及小故障的强度，因此分析师经常以每光谱的基础进行脱胶。已经提出了一些例程，但它们很容易选择光谱伪影的选择，并且通常不足以处理大型数据集。在这里，我们提出了一种统计坚固的算法，该算法以Python程序的形式实现，用于自动检测和清除可用于大量光谱的故障。它使用Savitzky-Golay过滤器来平滑光谱和广义的极端偏离测试以识别异常值。我们使用此算法实现了强大的，可重复的和选择性去除故障的。
Analysis of x-ray absorption spectroscopy (XAS) data often involves the removal of artifacts or glitches from the acquired signal, a process commonly known as deglitching. Glitches result either from specific orientations of monochromator crystals or from scattering by crystallites in the sample itself. Since the precise energy or wavelength location and the intensity of glitches in a spectrum cannot always be predicted, deglitching is often performed on a per spectrum basis by the analyst. Some routines have been proposed, but they are prone to arbitrary selection of spectral artifacts and are often inadequate for processing large data sets. Here we present a statistically robust algorithm, implemented as a Python program, for the automatic detection and removal of glitches that can be applied to a large number of spectra. It uses a Savitzky-Golay filter to smooth spectra and the generalized extreme Studentized deviate test to identify outliers. We achieve robust, repeatable, and selective removal of glitches using this algorithm.
