Parchments are a writing material made from treated animal skins used for centuries to record important writings such as legal documents. In Europe and West Asia, it was the main writing surface from around the 4th century to around the 15th century. Understanding the origin, making and degradation of this important material will help to preserve the written record of European civilisation. In this project, we will extend the application of non-invasive analysis to imaging methods of a larger range of relevant modalities such as reflectance and fluorescence hyperspectral imaging to record the statistical variation across a parchment and manuscripts to improve the accuracy of machine learning or AI analysis. 3D topography and tomography information will be recorded using the non-invasive imaging method of optical coherence tomography (OCT). The aim is to develop a workflow that utilises multimodal and multiscale (from microscopic to macroscopic) non-invasive imaging techniques to capture the range of relevant information, e.g. related to the animal species, age, part of the animal used, degradation and difference in the manufacturing process. An AI based method will be developed to disentangle the information. Where possible samples will be taken to be performed with the more precise proteomics analysis as reference and for verification of the results. In order to train the AI model, this project will be data-driven where a large collection of parchment/manuscripts will be analysed, which is only possible with non-invasive imaging techniques. Manuscripts of different dates, from regions of parchments exposed to the atmosphere to inner regions of a manuscript will be compared to understand the various markers.
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