In several manuscripts, text is obscured due to glued together leaves, making it difficult or impossible to read. In this context, this study explores the use of reflectance and transmittance imaging spectroscopy (RIS and TIS) in the visible and near-infrared range (400–1000 nm) to recover hidden texts. The method is applied to two cases of medieval Frisian legal codes from the Richthofen Collection, where we employed Non-Negative Matrix Factorization (NMF) for the analysis of single and spectrally fused datasets integrating both RIS and TIS. We further integrated spatial stitching of adjacent areas to enhance spatial resolution of the images. Our results demonstrate that factorization algorithms perform well on fused datasets, with spectral fusion proving essential in complex cases where individual analyses fail to clearly reveal hidden text. ... Read more

This work introduces a novel method to multivariate analysis applied to fused hyperspectral datasets in the field of Cultural Heritage (CH). Hyperspectral Imaging is a well-established approach for the non-invasive examination of artworks, offering insights into their composition and conservation status. In CH field, a combination of hyperspectral techniques is usually employed to reach a comprehensive understanding of the artwork. To deal with hyperspectral data, multivariate statistical methods are essential due to the complexity of the data. The process involves factorizing the data matrix to highlight components and reduce dimensionality, with techniques such as Non-negative Matrix Factorization (NMF) gaining prominence. To maximize the synergies between multimodal datasets, the fusion of hyperspectral datasets can be coupled with multivariate analysis, with potential applications in CH. In this work, I will show examples of this approach with different combinations of datasets, including reflectance and transmittance spectral imaging, Fluorescence Lifetime Imaging and Time-Gated Hyperspectral Imaging, and Raman and fluorescence spectroscopy micro-mapping. ... Read more