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Updated: Jun 20, 2026

Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters
Published on: June 18, 2021
FT-NIR spectroscopy for non-invasive identification of natural polymers and resins in easel paintings
M Vagnini1, C Miliani, L Cartechini
1Centro di Eccellenza SMAArt (Scientific Methodologies applied to Archaeology and Art), c/o Dipartimento di Chimica, Università degli Studi di Perugia, via Elce di Sotto 8, 06123 Perugia, Italy. manuela@thch.unipg.it
Abstract:
In the present study, the analytical strengths and limitations of near-infrared (NIR) spectroscopy to non-invasively characterize organic components in painting materials have been investigated. In spite of the increased amount of information available today from advanced modern analytical instrumentations dedicated to cultural heritage, the non-invasive identification of materials belonging to the wide class of organic compounds historically used in paintings is still a challenging task. Near-infrared spectroscopy offers several attractive features that make this technique particularly suitable to this purpose. In fact, it is non-invasive, allows for non-contact measurements in reflectance mode, gives molecular information on complex macromolecules, and can be performed on-site by means of portable devices. First-derivative transformation of reflectance spectroscopic data has been applied to provide a simple and fast way to deduce more information from NIR spectra. This approach has allowed spectral features to be identified that can be useful to distinguish different compounds belonging to the classes of lipids, proteins, and resins. To this purpose, at first, a spectral database of pure standard has been collected. Our analytical approach was then successfully validated on pictorial models reproducing the typical stratigraphy of an easel painting. As final step, the study of a real painting has been attempted and a drying oil, animal glue, and a terpenic natural resin, as well as an earth pigment were clearly identified, as cross-validated by GC-MS analysis.
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