MA-XRF datasets analysis based on convolutional neural network: A case study on religious panel paintings

Abstract

Macroscopic X-ray fluorescence (MA-XRF) datasets are analyzed using Artificial Neural Networks. Specifically, Convolutional Neural Networks (CNNs) are trained by coupling the spectra acquired during the MA-XRF scan of two religious panel paintings (“icons”) with the associated Ground-Truth counts per characteristic transition line, as they are extracted by X-ray fluorescence fundamental parameters analysis. In total, twenty thousand XRF spectra were used for the CNN training. The trained neural networks were applied to analyze millions of MA-XRF spectra acquired during the scan of religious painting panels by computing the counts per pixel of X-ray characteristic transition lines and creating the elemental transition maps. Comparison of the CNN extracted results to the Ground-Truth (GT) shows remarkable agreement. The successful MA-XRF datasets analysis applying the CNN method paves an analytical path to the direction of the auto-identification of spectral lines, offering the means for the non-experienced XRF analyst to provide a state-of-the-art analysis and supporting the experienced user not to overlook hardly resolved transition lines.