2023 atomic spectrometry update – a review of advances in X-ray fluorescence spectrometry and its special applications

Christine Vanhoof, Jeffrey R. Bacon, Ursula E. A. Fittschen and Laszlo Vincze

J. Anal. At. Spectrom., 2023, 38, 1730
DOI: 10.1039/D3JA90026F

Atomic spectrometry update: review of advances in the analysis of metals, chemicals and materials

Robert Clough, Andy Fisher, Bridget Gibson and Ben Russell

J. Anal. At. Spectrom., 2023, 38, 2215
DOI: 10.1039/D3JA90038J

Can deep learning assist automatic identification of layered pigments from XRF data?

Bingjie Jenny Xu, Yunan Wu, Pengxiao Hao, Marc Vermeulen, Alicia McGeachy, Kate Smith, Katherine Eremin, Georgina Rayner, Giovanni Verri, Florian Willomitzer, Matthias Alfeld, Jack Tumblin, Aggelos Katsaggelos and Marc Walton

J. Anal. At. Spectrom., 2022, 37, 2672
DOI: 10.1039/D2JA00246A

High-resolution compound-specific mapping in works of art via data fusion of MA-XRPD with hyperspectral data (part 1: Method evaluation)

Arthur Gestels, Francesca Gabrieli, Thomas De Kerf, Frederik Vanmeert, Hernan Fernández García, John Delaney, Koen Janssens, Gunther Steenackers and Steve Vanlanduit

Talanta, 2024, 280, 126731
DOI: 10.1016/j.talanta.2024.126731

Support Vector Machine Approach to the Spectroscopic Classification of Archaeological Bitumen Composites in Ancient Mesopotamia

Giulia Festa, C. Scatigno, V. Caruso, S. Giampaolo, A. Tufari, L. Ferguson, A. Greco, F. Manclossi and Licia Romano

J. Compos. Sci., 2025, 9, 596
DOI: 10.3390/jcs9110596

Deep learning for enhanced spectral analysis of MA-XRF datasets of paintings

Zdenek Preisler, Rosario Andolina, Andrea Busacca, Claudia Caliri, Costanza Miliani and Francesco P. Romano

Sci. Adv., 2024, 10
DOI: 10.1126/sciadv.adp6234

Machine learning for efficient grazing-exit x-ray absorption near edge structure spectroscopy analysis: Bayesian optimization approach

Cafer Tufan Cakir, Can Bogoclu, Franziska Emmerling, Christina Streli, Ana Guilherme Buzanich and Martin Radtke

Mach. Learn.: Sci. Technol., 2024, 5, 025037
DOI: 10.1088/2632-2153/ad4253

Machine learning in analytical chemistry for cultural heritage: A comprehensive review

Aleksandra Towarek, Ludwik Halicz, Stan Matwin and Barbara Wagner

Journal of Cultural Heritage, 2024, 70, 64
DOI: 10.1016/j.culher.2024.08.014

X-ray and neutron imaging for cultural heritage: the INFN-CHNet experience

A. Mazzinghi, L. Castelli, C. Ruberto, S. Barone, F. García-Avello Bofías, A. Bombini, C. Czelusniak, N. Gelli, F. Giambi, M. Manetti, M. Massi, L. Sodi, A. Balerna, L. Pronti, M. Romani, M. Angelucci, G. Viviani, V. Sciarra, M. Cestelli Guidi, M. Marabotto, L. Sottili, L. Vigorelli, F. Tansella, M. Magalini, L. Guidorzi, A. Re, A. Lo Giudice, C. Ricci, F. Pozzi, G. Iorio, V. Graziani, A. Fabbri, P. Branchini, A. De Carlo, L. Tortora, M. P. Morigi, M. Bettuzzi, R. Brancaccio, M. Seracini, F. Cantini, F. Grazzi, G. Marcucci, M. Clemenza, D. Alloni, S. Altieri, R. Rossini, A. Salvini, O. Sans-Planell, D. Zafiropoulos, L. Sarchiapone, F. Arneodo, R. A. Torres Saavedra, M. F. Castellá, N. Mastrangelo, A. M. Morales, F. Taccetti and L. Giuntini

Eur. Phys. J. Plus, 2024, 139
DOI: 10.1140/epjp/s13360-024-05429-z

Investigating Brazilian Paintings from the 19th Century by MA-XRF

André Pimenta, Valter Felix, Matheus Oliveira, Miguel Andrade, Marcelo Oliveira and Renato Freitas

QuBS, 2023, 7, 9
DOI: 10.3390/qubs7010009

An artificial intelligence-based semiquantitative method based on visible spectroscopy and imaging to analyse inorganic red pigments in wall paintings

Roberto Sáez-Hernández, Jordi Cruz, Manel Alcalà-Bernàrdez, Ángel Morales-Rubio and M. Luisa Cervera

Journal of Cultural Heritage, 2025, 75, 139
DOI: 10.1016/j.culher.2025.07.018

A cloud-native application for digital restoration of Cultural Heritage using nuclear imaging: THESPIAN-XRF

Alessandro Bombini, Fernando García-Avello Bofías, Chiara Ruberto and Francesco Taccetti

Rend. Fis. Acc. Lincei, 2023, 34, 867
DOI: 10.1007/s12210-023-01174-0

XMapsLab: A program for the creation and study of maps for Cultural Heritage

Domingo Martín, Germán Arroyo, Juan Ruiz de Miras, Luis López, María Rosario Blanc, José Luis Vílchez, Philippe Sarrazin and Juan Carlos Torres

Journal of Cultural Heritage, 2025, 73, 1
DOI: 10.1016/j.culher.2025.02.011

Probing creation process of paintings from Tarsila do Amaral (1886–1973) by MA-XRF and differential X-ray attenuation

Mônica Grôppo Parma, Isolda de Castro Mendes, André Rocha Pimenta, Ana Letícia Castro de Oliveira, Valter de Souza Felix, Heloisa Beraldo, Nilton Thaumaturgo Rocha Junior, Claudia Regina Ferreira de Souza, Rafael Dias Mayer, Aline Paula de Oliveira, Matheus Batista de Oliveira, Miguel Andrade de Oliveira and Renato Pereira de Freitas

Radiation Physics and Chemistry, 2023, 212, 111191
DOI: 10.1016/j.radphyschem.2023.111191

A new segmentation-assisted interpolation method for creating maps in the study of artworks

Domingo Martín, Germán Arroyo, Juan Carlos Torres, Luis López, María Rosario Blanc and Juan Ruiz de Miras

Chemometrics and Intelligent Laboratory Systems, 2025, 264, 105466
DOI: 10.1016/j.chemolab.2025.105466

MA‐XRF Scanner: Implementation of Elemental Maps Reconstruction Algorithm Based on Single Photon Detection for Cultural Heritage Studies

Luis Guillermo García Ordóñez, Crespo Maria Liz, Andres Cicuttin, Ivan Rene Morales Argueta, Romina Molina, Sergio Carrato, Piet Van Espen, Paolo Romano, Alessandro Migliori, Roman Padilla, Mladen Bogovac and Kalliopi Kanaki

X-Ray Spectrometry, 2025, 54, 579
DOI: 10.1002/xrs.3496

Advanced X-ray techniques to study the alteration of pigments in paintings

Letizia Monico, Koen Janssens, Marine Cotte, Samuel M. Webb, Frederik Vanmeert, Victor Gonzalez, Geert Van der Snickt, Katrien Keune, Brunetto Giovanni Brunetti, Gerald Falkenberg, Jan Garrevoet, Annelies van Loon, Marc Vermeulen, Maite Maguregui, Steven De Meyer, Ermanno Avranovich Clerici, Fréderique T. H. Broers, Laura Cartechini, Nouchka De Keyser, Francesco d’Acapito, Florian Meirer, Aldo Romani, Francesca Rosi, Sara Carboni Marri, Daniela Comelli, Nina Deleu, Ida Fazlic, Marta Ghirardello, Clément Holé, Silvia Pérez-Diez, Mathieu Thoury and Costanza Miliani

Riv. Nuovo Cim., 2025, 48, 315
DOI: 10.1007/s40766-025-00070-7

Datacube segmentation via deep spectral clustering

Alessandro Bombini, Fernando García-Avello Bofías, Caterina Bracci, Michele Ginolfi and Chiara Ruberto

Mach. Learn.: Sci. Technol., 2024, 5, 035024
DOI: 10.1088/2632-2153/ad622f

Machine learning for painting conservation: a state-of-the-art review

Aster Van Vijle, Piraye Hacıgüzeller and Geert Van der Snickt

npj Herit. Sci., 2025, 13
DOI: 10.1038/s40494-025-01924-3

Advances in Analytical Methods for Cultural Heritage

Federica Pozzi and Catherine H. Stephens

Applied Sciences, 2024, 14, 7587
DOI: 10.3390/app14177587

Neural Networks for Hyperspectral Imaging of Historical Paintings: A Practical Review

Lingxi Liu, Tsveta Miteva, Giovanni Delnevo, Silvia Mirri, Philippe Walter, Laurence de Viguerie and Emeline Pouyet

Sensors, 2023, 23, 2419
DOI: 10.3390/s23052419

A deep learning approach for improving ore classification accuracy by double-stream heterogeneous data fusion of XRF

Zenan Xiao, Fusheng Li, Shubin Lyu and Qianlong Wang

Microchemical Journal, 2026, 220, 116506
DOI: 10.1016/j.microc.2025.116506

Evaluation of interpolation methods for generating maps in cultural heritage chemical applications

Domingo Martín, Germán Arroyo, Juan Ruiz de Miras, Luis López, María Rosario Blanc, Philippe Sarrazin and Juan Carlos Torres

Journal of Cultural Heritage, 2023, 62, 293
DOI: 10.1016/j.culher.2023.06.004

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

Theofanis Gerodimos, Ioannis Georvasilis, Anastasios Asvestas, Georgios P. Mastrotheodoros, Aristidis Likas and Dimitrios F. Anagnostopoulos

Chemometrics and Intelligent Laboratory Systems, 2024, 250, 105138
DOI: 10.1016/j.chemolab.2024.105138

Towards virtual painting recolouring using vision transformer on x-ray fluorescence datacubes

Alessandro Bombini, Fernando García-Avello Bofías, Francesca Giambi and Chiara Ruberto

Mach. Learn.: Sci. Technol., 2025, 6, 015058
DOI: 10.1088/2632-2153/adb937