TY - JOUR PB - SPRINGER N1 - This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. SN - 2050-7445 Y1 - 2022/06/13/ TI - Neural network-based classification of X-ray fluorescence spectra of artists' pigments: an approach leveraging a synthetic dataset created using the fundamental parameters method EP - 14 AV - public N2 - X-ray fluorescence (XRF) spectroscopy is an analytical technique used to identify chemical elements that has found widespread use in the cultural heritage sector to characterise artists' materials including the pigments in paintings. It generates a spectrum with characteristic emission lines relating to the elements present, which is interpreted by an expert to understand the materials therein. Convolutional neural networks (CNNs) are an effective method for automating such classification tasks?an increasingly important feature as XRF datasets continue to grow in size?but they require large libraries that capture the natural variation of each class for training. As an alternative to having to acquire such a large library of XRF spectra of artists' materials a physical model, the Fundamental Parameters (FP) method, was used to generate a synthetic dataset of XRF spectra representative of pigments typically encountered in Renaissance paintings that could then be used to train a neural network. The synthetic spectra generated?modelled as single layers of individual pigments?had characteristic element lines closely matching those found in real XRF spectra. However, as the method did not incorporate effects from the X-ray source, the synthetic spectra lacked the continuum and Rayleigh and Compton scatter peaks. Nevertheless, the network trained on the synthetic dataset achieved 100% accuracy when tested on synthetic XRF data. Whilst this initial network only attained 55% accuracy when tested on real XRF spectra obtained from reference samples, applying transfer learning using a small quantity of such real XRF spectra increased the accuracy to 96%. Due to these promising results, the network was also tested on select data acquired during macro XRF (MA-XRF) scanning of a painting to challenge the model with noisier spectra Although only tested on spectra from relatively simple paint passages, the results obtained suggest that the FP method can be used to create accurate synthetic XRF spectra of individual artists' pigments, free from X-ray tube effects, on which a classification model could be trained for application to real XRF data and that the method has potential to be extended to deal with more complex paint mixtures and stratigraphies. JF - Heritage Science UR - https://doi.org/10.1186/s40494-022-00716-3 VL - 10 A1 - Jones, Cerys A1 - Daly, Nathan S A1 - Higgitt, Catherine A1 - Rodrigues, Miguel RD KW - Arts & Humanities KW - Science & Technology KW - Physical Sciences KW - Technology KW - Humanities KW - Multidisciplinary KW - Chemistry KW - Analytical KW - Materials Science KW - Multidisciplinary KW - Spectroscopy KW - Arts & Humanities - Other Topics KW - Chemistry KW - Materials Science KW - X-ray fluorescence KW - Convolutional neural networks KW - Deep learning KW - Transfer learning KW - Classification KW - Synthetic data KW - Fundamental parameters KW - Pigment KW - Painting KW - INTENSITIES KW - PAINTINGS KW - ORIGIN ID - discovery10165753 ER -