Machine learning regression algorithms for generating chemical element maps from Xray fluorescence data of paintings Ruiz de Miras, Juan Gacto Colorado, María José Blanc García, María Rosario Arroyo Moreno, Germán López Escudero, Luis Torres Cantero, Juan Carlos Martín Perandrés, Domingo Machine learning Random forest Regression problem X-ray fluorescence Chemical element maps Generating chemical element maps of paintings from X-ray fluorescence (XRF) data is a very valuable tool for the scientific community of conservators and art historians. Hand-held XRF scanners are cheap and easily portable but their use provides scans with a few data, so additional analytical tools are needed to obtain reliable chemical element maps from them. Recently, the software tool SmART_Scan was released, which uses an algorithm based on the minimum hypercube distance (MHD) to compute this kind of maps. In this paper, we propose a new methodology to address this problem by using machine learning algorithms for regression as alternative and more accurate techniques than MHD. We tested MHD versus eight machine learning regression algorithms on two paintings with different features. Our results showed that machine learning algorithms Random Forest and kNN significantly outperformed MHD in Mean Squared Error (MSE) and coefficient of determination (R2) for all the experiments. When using experts’ data and a hold-out validation, kNN was the best-ranked algorithm. Random Forest was the best-ranked algorithm when cross-validation was used. We did not find significant differences in average MSE nor in R2 between kNN and Random Forest, so we can conclude that Random Forest is the best-suited algorithm for computing chemical element maps of paintings from XRF data. 2024-04-05T06:56:35Z 2024-04-05T06:56:35Z 2024-05-15 info:eu-repo/semantics/article Juan Ruiz de Miras, María José Gacto, María Rosario Blanc, Germán Arroyo, Luis López, Juan Carlos Torres, Domingo Martín, Machine learning regression algorithms for generating chemical element maps from X-ray fluorescence data of paintings, Chemometrics and Intelligent Laboratory Systems, Volume 248, 2024, 105116, ISSN 0169-7439, https://doi.org/10.1016/j.chemolab.2024.105116 https://hdl.handle.net/10481/90401 10.1016/j.chemolab.2024.105116 eng http://creativecommons.org/licenses/by-nd/4.0/ info:eu-repo/semantics/openAccess Attribution-NoDerivatives 4.0 Internacional Elsevier