1. Deciphering the Chemistry of Cultural Heritage: Targeting Material Properties by Coupling Spectral Imaging with Image Analysis
- Author
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Étienne Anheim, Loïc Bertrand, Pierre Gueriau, Mathieu Thoury, Serge X. Cohen, Photophysique et Photochimie Supramoléculaires et Macromoléculaires (PPSM), Institut de Chimie du CNRS (INC)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Ecole Normale Supérieure Paris-Saclay (ENS Paris Saclay), Institut photonique d'analyse non-destructive européen des matériaux anciens (IPANEMA), Muséum national d'Histoire naturelle (MNHN)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Centre National de la Recherche Scientifique (CNRS)-Ministère de la Culture (MC), Nestlé Institute of Health Sciences SA [Lausanne, Switzerland], Centre de Recherches Historiques (CRH), École des hautes études en sciences sociales (EHESS)-Centre National de la Recherche Scientifique (CNRS), and We thank Barbara Berrie (National Gallery of Art, Washington, D.C., U.S.) and Agnès Desolneux (Centre Borelli, ENS Paris-Saclay, CNRS) for their careful rereading of our manuscript. The authors are particularly grateful to the large number of colleagues with whom we have discussed these aspects over many years of common work and interaction. Our very special thanks go to Marie-Angélique Languille (today at the Centre de Recherche sur la Conservation des Collections, Paris) and Sophie David (PPSM). We thank all of our coauthors from the papers discussed and the many colleagues with whom we have discussed these ideas, including Demetrios Anglos, Julie Arslanoglu, Bernadette Bensaude-Vincent, Uwe Bergmann, Barbara Berrie, Catherine Brechignac, Gilles Celeux, Antoine Chambaz, Pierre Chastang, Cynthia Colmellere, Marie Cornu, Jean-Paul Demoule, Agnès Desolneux, Jean-Philippe Échard, Douglas Galante, Pierre Galtier, Claire Gervais, Denis Gratias, Agnès Grimaud, Charlotte Guichard, Ineke Joosten, Katrien Keune, Andrew King, Bertrand Lavédrine, Pierre Laszlo, Erwan Le Pennec, Pierre Levitz, Alain Lusson, Lara Maldanis, Pascal Massard, Cristian Mocuta, Lionel Moisan, Emmanuel (Manolis) Pantos, André Rassat, Matthieu Réfrégiers, Luc Robbiola, Laurent Romary, Isabelle Rouget, Jean-Pascal Rueff, Solenn Réguer, Clément Sanchez, Sebastian Schoeder, Marika Spring, Maartje Stols-Witlox, Matija Strlic, Caroline Tokarski, Edward Vicenzi, Laurence de Viguerie, Kees van der Beek, Robert van Langh, Philippe Walter, Sam Webb, and many friends, students, and colleagues. L.B. acknowledges the support of the Fondation des Treilles and its wonderful team for the organization of two seminars (in 2013 and 2014) that enabled the scope of this research program to be critically defined. The construction of the IPANEMA laboratory was funded by a CPER grant from the French Ministère de la recherche, de l’enseignement supérieur et de l’innovation and Région Île-de-France.
- Subjects
medicine.medical_specialty ,Data processing ,Materials science ,010405 organic chemistry ,Chemistry ,media_common.quotation_subject ,Hyperspectral imaging ,General Medicine ,General Chemistry ,Ambiguity ,010402 general chemistry ,01 natural sciences ,Data science ,0104 chemical sciences ,Spectral imaging ,Characterization (materials science) ,Cultural heritage ,Identification (information) ,medicine ,[CHIM]Chemical Sciences ,Spatial analysis ,media_common - Abstract
International audience; ConspectusThe 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.
- Published
- 2021