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Deciphering the Chemistry of Cultural Heritage: Targeting Material Properties by Coupling Spectral Imaging with Image Analysis

Cite this: Acc. Chem. Res. 2021, 54, 13, 2823–2832
Publication Date (Web):June 18, 2021
https://doi.org/10.1021/acs.accounts.1c00063
Copyright © 2021 American Chemical Society

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    Abstract

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    The chemical study of materials from natural history and cultural heritage, which provide information for art history, archeology, or paleontology, presents a series of specific challenges. The complexity of these ancient and historical materials, which are chemically heterogeneous, the product of alteration processes, and inherently not reproducible, is a major obstacle to a thorough understanding of their making and long-term behavior (e.g., fossilization). These challenges required the development of methodologies and instruments coupling imaging and data processing approaches that are optimized for the specific properties of the materials. This Account discusses how these characteristics not only constrain their study but also open up specific innovative avenues for providing key historical information. Synchrotron methods have extensively been used since the late 1990s to study heritage objects, in particular for their potential to provide speciation information from excitation spectroscopies and to image complex heritage objects and samples in two and three dimensions at high resolution. We examine in practice how the identification of key intrinsic chemical specificities has offered fertile ground for the development of novel synchrotron approaches allowing a better stochastic description of the properties of ancient and historical materials. These developments encompass three main aspects: (1) The multiscale heterogeneity of these materials can provide an essential source of information in the development of probes targeting their multiple scales of homogeneity. (2) Chemical alteration can be described in many ways, e.g., by segmenting datasets in a semiquantitative way to jointly inform morphological and chemical transformation pathways. (3) The intrinsic individuality of chemical signatures in artifacts triggers the development of specific strategies, such as those focusing on weak signal detection. We propose a rereading of the advent of these new methodologies for analysis and characterization and examine how they have led to innovative strategies combining materials science, instrument development, history, and data science. In particular, we show that spectral imaging and the search for correlations in image datasets have provided a powerful way to address what archeologists have called the uncertainty and ambiguity of the material record. This approach has implications beyond synchrotron techniques and extends in particular to a series of rapidly developing approaches that couple spectral and spatial information, as in hyperspectral imaging and spatially resolved mass spectrometry. The preeminence of correlations holds promise for the future development of machine learning methods for processing data on historical objects. Beyond heritage, these developments are an original source of inspiration for the study of materials in many related fields, such as environmental, geochemical, or life sciences, which deal with systems whose alteration and heterogeneity cannot be neglected.

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    Cited By

    This article is cited by 8 publications.

    1. Clémence Iacconi, Jonathan Piard, Elena Tosi-Brandi, François Azambourg, Marion Dubois, Vincent Créance, Loïc Bertrand. Training Designers in Archeological Chemistry As Part of a Project-Based Studio. Journal of Chemical Education 2023, Article ASAP.
    2. Loïc Bertrand, Sebastian Schöder, Ineke Joosten, Samuel M. Webb, Mathieu Thoury, Thomas Calligaro, Étienne Anheim, Aliz Simon. Practical advances towards safer analysis of heritage samples and objects. TrAC Trends in Analytical Chemistry 2023, 164 , 117078. https://doi.org/10.1016/j.trac.2023.117078
    3. Clémence Iacconi, Awen Autret, Elsa Desplanques, Agathe Chave, Andrew King, Barbara Fayard, Christophe Moulherat, Émilie Leccia, Loïc Bertrand. Virtual technical analysis of archaeological textiles by synchrotron microtomography. Journal of Archaeological Science 2023, 149 , 105686. https://doi.org/10.1016/j.jas.2022.105686
    4. N. Torezhanova, O. Myakisheva, B. Mukhametuly, M. Kenessarin, R. Baitugulov, A. K. Bekbayev, K. M. Nazarov. Neutron-tomographic study of the structural features of a bronze mirror found in the Akterek burial complex. Eurasian Journal of Physics and Functional Materials 2022, 6 (4) , 266-274. https://doi.org/10.32523/ejpfm.2022060402
    5. Solenn Reguer, Sebastian Schöder, Delphine Vantelon, Timm Weitkamp, Jean-Pascal Rueff, Felisa Berenguer, Andrew King, Frederic Jamme, Myrtille O. J. Y. Hunault, Mathieu G. Silly, Nicolas Trcera, Matthieu Refregiers. Fifteen Years of Study of Cultural and Natural Heritage Materials at SOLEIL. Synchrotron Radiation News 2022, 35 (5) , 10-20. https://doi.org/10.1080/08940886.2022.2135959
    6. Emille Martinazzo Rodrigues, Eva Hemmer. Trends in hyperspectral imaging: from environmental and health sensing to structure-property and nano-bio interaction studies. Analytical and Bioanalytical Chemistry 2022, 414 (15) , 4269-4279. https://doi.org/10.1007/s00216-022-03959-y
    7. Gilles Celeux, Serge X. Cohen, Agnès Grimaud, Pierre Gueriau. Hierarchical Clustering of Spectral Images with Spatial Constraints for the Rapid Processing of Large and Heterogeneous Data Sets. SN Computer Science 2022, 3 (3) https://doi.org/10.1007/s42979-022-01074-4
    8. Ruxandra Stoean, Nebojsa Bacanin, Leonard Ionescu, Catalin Stoean, Marinela Boicea, Alina-Maria Garau, Cristina-Camelia Ghitescu. Deep learning for a swift non-invasive recognition and delineation of corrosive iron compounds present on the surface of unrestored archaeological artefacts. Procedia Computer Science 2022, 207 , 1303-1311. https://doi.org/10.1016/j.procs.2022.09.186

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