Imaging and spectroscopic data combined to disclose the painting techniques and materials in the fifteenth century Leonardo atelier in Milan. (March 2021) - Galli, Anna; Gargano, Marco; Bonizzoni, Letizia; Bruni, Silvia; Interlenghi, Matteo; Longoni, Margherita; Passaretti, Arianna; Caccia, Michele; Salvatore, Christian; Castiglioni, Isabella; Martini, Marco
Abstract: The project "Leonardesque Artists beyond the Visible" was planned on the 500th anniversary of the death of Leonardo da Vinci, with the occasion of an exhibition held in Milan in 2019, where more than twenty works made by Leonardesque masters have been put on display. Among them, five representative paintings made by his closest pupils were selected for a comprehensive and multidisciplinary project. Portable non-invasive imaging and spectroscopy techniques were applied to supply useful information to scholars but also to the wider public: description of the material composition of the pigments, of the preparation and of the binders, existence or absence of underdrawing, and identification of the painters' technique and style. Particular attention was paid also to the image processing techniques, mostly for hyperspectral and radiographic data, to get the most from both innovative and traditional techniques. Results highlighted for each author a peculiar painting technique showing hidden features such as pentimenti and the panel preparation methods, pigments, binders and varnishes. Graphical abstract: Image 1 Highlights: We present the analytical results obtained in the framework of the "Leonardesque Artists beyond the Visible" project. We validate an analytical protocol for a complete characterization of materials and painting processes. We apply portable non-invasive imaging and spectroscopy techniques detecting both organic and inorganic materials. The innovative method for processing hyperspectral images results in a complete, non-invasive, survey of paintings. Appropriate mathematical processes on Radiographic images open a new way to the elaboration of data.