Authors
Francesca Rosi, Laura Cartechini, David Buti, Francesca Sabatini, Aldo Romani, Diego Sali, Xia Wu, Roland Harig, M Cristina Tomassetti, Brunetto G Brunetti, Costanza Miliani
Published in
ACS sensors. Jul 15, 2025. Epub Jul 15, 2025.
Abstract
The identification of the chemical composition of painting materials and their macroscale distribution is crucial for effective conservation, knowledge, and enhancement of our irreplaceable cultural patrimony. The noninvasive comprehensive characterization of both organic and inorganic components in paintings is currently achieved by mid-infrared (mid-IR) spectroscopy at single points or by chemical mapping via point-by-point scans over areas of square centimeters. However, the lengthy acquisition time required for mapping measurements makes it difficult to record chemical distribution images within a reasonable time frame. This limitation underscores the need for a hyperspectral imaging approach. To address this challenge, an advanced hyperspectral imager operating in the mid-IR spectral range (4000-800 cm-1) is presented here, providing compositional images of great value for the study and conservation of paintings. The system allows heritage scientists to record, within a few minutes, accurate distributions of inorganic and organic components, most importantly of degradation products, and it enables real-time monitoring of restoration intervention. The imager was first validated through mock-ups of known composition by direct comparison of spectra extracted from the hypercube data set with those acquired in the same areas by a portable single-point IR spectrometer. Then, insights into the nature and localization of original and degradation materials were provided for a precious Renaissance painting under restoration through in situ measurements. The results achieved put in evidence how the advances of mid-IR hyperspectral imaging represent a significant step ahead in the scientific examination of tangible cultural heritage.
PMID:
40663051
Bibliographic data and abstract were imported from PubMed on 15 Jul 2025.
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