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. Parchment is processed from animal skin, a fibrous material containing mainly collagen which degrades with time as any biological material. Over centuries, collagen in parchments suffers denaturation (through oxidation and hydrolysis) which can lead ultimately to its gelatinization.

The identification of animal species from which parchment originates (calf, sheep or goat) in connection with the making of manuscripts and their economic value has given rise to a new discipline: biocodicology. Zoological mass spectrometry (ZooMS), an invasive technique, has long been used for identification of animal species in archaeological samples at the molecular level and its more recent micro-invasive variant (eZooMS) has been applied to historical manuscripts. More recently, in a limited preliminary study, machine learning (ML) applied to the non-invasive optical technique of reflectance spectroscopy succeeded in identifying species in historical manuscripts, demonstrating the potential of non-invasive optical techniques aided by AI in parchment studies.

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. Measurements will be carried out on Belgium and British historical archives with different manufacturing tradition. The project will benefit our understanding of one of the most important writing materials – a method to date a document based on the parchment and provide surveys of the conservation state of the material.