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.

中文翻译：

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一种使用神经网络解释 XRF 光谱成像数据的新方法

自组织图 (SOM) 是一种基于神经网络的无监督机器学习算法，用于引入一种分析 XRF 光谱成像数据的新方法。这种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簇。在这项研究中，我们展示了如何使用聚类以及空间和光谱信息的组合来帮助识别材料并推断油漆序列。通过对盖蒂研究所收藏的秘鲁水彩画的分析，展示了该方法的效率和准确性。解释的确认由互补的非侵入性技术提供，例如光学显微镜、反射和拉曼光谱。