Imaging spectroscopy methods are becoming increasingly relevant in the field of cultural heritage science. The datacubes output by these methods represent some of the most significant challenges related to their application, namely howto make sense of complex multi-dimensional datasets. As the selection of imaging spectroscopy methods and the complexity of the resulting datasets continue to grow, the time required to conduct all these measurements and process all of the data also increases. This research focuses on addressing both the problem of extended data acquisition times and the challenge of processing the complex datacubes.

Firstly, chapter 1 introduces the basic concepts of cultural heritage and the field of cultural heritage science. It also provides an overview of some of the scientific methods used for the study of cultural heritage objects, with a focus on imaging methods, and particularly imaging spectroscopy methods. The two primary methods considered in this work, macro X-ray fluorescence spectroscopy (MA-XRF) and reflectance imaging spectroscopy (RIS), are described in greater detail and an overview of the state-of-the-art in equipment and data processing methods is provided.

Beginning with the issue of data processing, chapter 2 discusses a novel method for the use of short-wave infrared (SWIR) (1000–2500nm) RIS for semi-quantitative analysis of historical paintings. The method consists of the isolation and deconvolution of characteristic absorption features of target pigments. The concept is proven on two pigments, lead white and blue verditer. The method is tested on a set of specially prepared paint samples as well as a 16th century painting. The method is compared to MA-XRF on its ability to selectively map pigments as well as its ability to provide quantitative information on the pigment concentrations. A novel data visualization method, able to visualize chemical relevance of individual pixels whilst also highlighting larger spatial patterns, is also presented.

Chapter 3 continues on the topic of data processing, presenting a novel approach for the analysis of damaged historical manuscripts through the use of visible and nearinfrared (VNIR) (400–1000 nm) RIS combined with supervised machine learning methods and machine learning explainability methods. The approach uses manually labeled VNIR RIS data acquired from historical manuscripts to train an XGBoost classification model, followed by the application of Shapley additive explanations (SHAP) to analyse the behaviour of the classification model. The calculated SHAP values are then used to calculate a SHAP-weighted intensity map (SWIM), which is found to improve the legibility of the analysed manuscripts. An adaptive colour scheme is also proposed as a method of easing the evaluation by paleographists of the resulting images. The approach is tested on two texts, the Leiden Riddle, a 9-10th century Northumbrian text, and the 1669 First Set of the Fundamental Constitutions of Carolina, the earliest text attributed to political philosopher John Locke.

Shifting to the issue of data acquisition, chapter 4 documents the design and testing of two MA-XRF scanners. These scanners, the big lead box (BLB) Mark I and Mark II, are designed to be flexiblemeasurement platforms that allow for the testing of alternate scanning strategies and multi-modal acquisitions. Highlighted are the safety features implemented into the design of the scanners with the goal of minimizing potential radiation exposure to users and bystanders.

Following the development of the MA-XRF scanners, chapter 5 discusses two methods for the acceleration of MA-XRF measurements through the use of smart scanning strategies. The first method, the Fast Autonomous Scanning Toolkit (FAST), uses machine learning models to dynamically select which pixels to scan whilst trying to reduce the estimated distortion between the reconstructed data set and the underlying ground truth. The second method, the Chopp algorithm, uses a double scan approach, where a first fast initial scan is used to estimate the optimal scan time per pixel for a second scan with a specified total scan time. The two methods are evaluated on their scan times and the achieved data quality compared to each other and a traditional raster scan.

Lastly, chapter 6 provides some concluding remarks on the themes covered during this work and highlights potential future developments.

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.

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.

Macroscopic x-ray fluorescence imaging spectroscopy (MA-XRF) and reflectance imaging spectroscopy (RIS) are important tools in the analysis of cultural heritage objects, both for conservation and art historical research purposes. The elemental and molecular distributions provided by MA-XRF and RIS respectively, are particularly useful for the identification and mapping of pigments in easel paintings. While MA-XRF has relatively established data processing methods based on modeling of the underlying physics, RIS data cannot be modeled with sufficient precision and its processing has considerable room for improvements. This work seeks to improve RIS data processing workflows in the short wavelength infrared range (SWIR, 1000–2500 nm) with a novel method that fits Gaussian profiles to pigment-specific absorption features, and we compare its performance to MA-XRF for the task of semi-quantitative pigment mapping, evaluating their limits of detection (LODs) and the matrix effects that affect their signals. Two pigments are considered in this work, lead white and blue verditer, which are mapped in SWIR RIS using the first overtone of -OH stretching of their primary compounds, hydrocerussite (Pb₃(CO₃)₂(OH)₂) and azurite (Cu₃(CO₃)₂(OH)₂), at 1447 and 1497 nm respectively, and in MA-XRF using the Pb-L and Cu-K fluorescence signals. The methods are evaluated using two sets of custom-prepared paint samples, as well as a 16th-century painting, discussing the identification, mapping, and semi-quantitative analysis of the considered pigments. We found SWIR RIS to be a pigment-specific method with a longer linear range but inferior LODs and penetration depth when compared to MA-XRF, the latter is often not capable of discriminating between different pigments with identical elemental markers. We furthermore present a novel color scale that allows the simultaneous visualization of signals above and below a confidence limit.