A new approach to the interpretation of XRF spectral imaging data using neural networks
A new approach to the interpretation of XRF spectral imaging data using neural networks
复制标题
使用神经网络解释 XRF 光谱成像数据的新方法
DOI：
10.1002/xrs.3188
复制
发表时间：
2020
影响因子：
1.2
通讯作者：
Kogou S
中科院分区：
文献类型：
--
作者：
Kogou S
自组织映射（SOM），一种基于神经网络的无监督机器学习算法，被用来介绍一种新的方法来分析XRF光谱成像数据。该方法自动将光谱图像数据集中的数十万个XRF光谱减少到少数共享相似光谱的不同聚类。在这项研究中，我们展示了如何聚类和空间和光谱信息的组合可以用来帮助材料识别和推断油漆序列。的效率和准确性的方法是通过分析从盖蒂研究所收集的秘鲁水彩画。通过补充的非侵入性技术，如光学显微镜、反射和拉曼光谱，对解释进行了确认。
Self‐organising map (SOM), an unsupervised machine learning algorithm based on neural networks, is applied to introduce a novel approach for the analysis of XRF spectral imaging data. This method automatically reduced hundreds of thousands of XRF spectra in a spectral image dataset to a handful of distinct clusters that share similar spectra. In this study, we show how clustering and the combination of spatial and spectral information can be used to aid materials identification and deduce the paint sequence. The efficiency and accuracy of the method is presented through the analysis of a Peruvian watercolour painting from the Getty Research Institute collection. Confirmation of the interpretation was provided by complementary non‐invasive techniques, such as optical microscopy, reflectance and Raman spectroscopies.