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Artificial Intelligence Analysis of Macroscopic X-Ray Fluorescence Data: A Case Study of Nineteenth Century Icon

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Advanced Nondestructive and Structural Techniques for Diagnosis, Redesign and Health Monitoring for the Preservation of Cultural Heritage (TMM 2023)

Part of the book series: Springer Proceedings in Materials ((SPM,volume 33))

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Abstract

This work comprehensively reviews artificial intelligence (AI) methods for macroscopic X-ray fluorescence (MA-XRF) data analysis of a religious panel painting (icon). ΜΑ-XRF is a powerful analytical imaging technique used to determine the elemental distribution maps of inhomogeneous targets. For the data analysis, we apply clustering algorithms such as k-means, factorization methods such as principal component analysis (PCA) and non-negative matrix factorization (NMF), and basic supervised machine learning methods, such as k-nearest neighbor (k-NN) regression and multilayer perceptron (MLP) regression. The applied AI methods allow for detailed and fast data analysis, providing two-dimensional elemental maps. The methods are beneficial for inexperienced users as they can analyze the MA-XRF data without detailed knowledge of the involved physics.

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Acknowledgements

This research was supported by project “Dioni: Computing Infrastructure for Big-Data Processing and Analysis” (MIS No. 5047222) co-funded by European Union (ERDF) and Greece through Operational Program “Competitiveness, Entrepreneurship and Innovation”, NSRF 2014–2020.

Special thanks are due to M. Ziagkos for providing access to icon from private collection.

Author information

Authors and Affiliations

  1. Department of Material Science and Engineering, University of Ioannina, Ioannina, Greece

    T. Gerodimos, D. Chatzipanteliadis, A. Asvestas, G. Mastrotheodoros & D. F. Anagnostopoulos

  2. Department of Computer Science and Engineering, University of Ioannina, Ioannina, Greece

    G. Chantas & A. Likas

  3. Department of Conservation of Antiquities and Works of Art, West Attica University, Aigaleo, Greece

    G. Mastrotheodoros

Authors
  1. T. Gerodimos
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  2. D. Chatzipanteliadis
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  3. G. Chantas
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  4. A. Asvestas
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  5. G. Mastrotheodoros
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  6. A. Likas
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  7. D. F. Anagnostopoulos
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Correspondence to T. Gerodimos .

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Editors and Affiliations

  1. Fraunhofer Institute for Nondestructive, Saarland University of Applied Sciences, Saarbrücken, Germany

    Ahmad Osman

  2. National Technical University of Athens, Athens, Greece

    Antonia Moropoulou

  3. National Technical University of Athens, Athens, Greece

    Kyriakos Lampropoulos

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Gerodimos, T. et al. (2024). Artificial Intelligence Analysis of Macroscopic X-Ray Fluorescence Data: A Case Study of Nineteenth Century Icon. In: Osman, A., Moropoulou, A., Lampropoulos, K. (eds) Advanced Nondestructive and Structural Techniques for Diagnosis, Redesign and Health Monitoring for the Preservation of Cultural Heritage. TMM 2023. Springer Proceedings in Materials, vol 33. Springer, Cham. https://doi.org/10.1007/978-3-031-42239-3_3

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