A new approach to the interpretation of XRF spectral imaging data using neural networks. (9th August 2020)

• Record Type: Journal Article
• Authors: Kogou, Sotiria; Lee, Lynn; Shahtahmassebi, Golnaz; Liang, Haida
• Other Names: Romano Francesco Paulo guestEditor.
• Is Part Of: X-ray spectrometry. Volume 50:Number 4(2021)
• Page Start: 310
• Page End: 319
• Publication Date: 2020-08-09
• Subjects: X-ray spectroscopy -- Periodicals; 543.62
• DOI: 10.1002/xrs.3188
• Languages: English
• ISSNs: 0049-8246

Abstract

Abstract : 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.