Palaeoproteomics guidelines to identify proteinaceous binders in artworks following the study of a 15th-century painting by Sandro Botticelli's workshop.

Undertaking the conservation of artworks informed by the results of molecular analyses has gained growing importance over the last decades, and today it can take advantage of state-of-the-art analytical techniques, such as mass spectrometry-based proteomics. Protein-based binders are among the most common organic materials used in artworks, having been used in their production for centuries. However, the applications of proteomics to these materials are still limited. In this work, a palaeoproteomic workflow was successfully tested on paint reconstructions, and subsequently applied to micro-samples from a 15th-century panel painting, attributed to the workshop of Sandro Botticelli. This method allowed the confident identification of the protein-based binders and their biological origin, as well as the discrimination of the binder used in the ground and paint layers of the painting. These results show that the approach is accurate, highly sensitive, and broadly applicable in the cultural heritage field, due to the limited amount of starting material required. Accordingly, a set of guidelines are suggested, covering the main steps of the data analysis and interpretation of protein sequencing results, optimised for artworks.

Artificial intelligence for art investigation: Meeting the challenge of separating x-ray images of the Ghent Altarpiece.

X-ray images of polyptych wings, or other artworks painted on both sides of their support, contain in one image content from both paintings, making them difficult for experts to "read." To improve the utility of these x-ray images in studying these artworks, it is desirable to separate the content into two images, each pertaining to only one side. This is a difficult task for which previous approaches have been only partially successful. Deep neural network algorithms have recently achieved remarkable progress in a wide range of image analysis and other challenging tasks. We, therefore, propose a new self-supervised approach to this x-ray separation, leveraging an available convolutional neural network architecture; results obtained for details from the Adam and Eve panels of the Ghent Altarpiece spectacularly improve on previous attempts.