Deliberately mutilated weapons and other objects are repeatedly discovered in ancient burials from the Iron Age. This research is focused on the Early Iron Age bent swords from the Hallstatt C period (800-600 BC) found in archaeological sites in the Netherlands. Metallographic research methods are used to investigate how these swords were bent, i.e., using a blacksmith’s fire or brute force. This elaborates on the Early Iron Age culture as it infers what kind of knowledge and skills were required for the bending process. With the help of a blacksmith, we created a replica to analyse the effect of different types of bending on the microstructure. This is compared with museum samples. Using optical microscopy and SEM(-EBSD) the microstructure of the museum sample and the replica are analysed for signs of deformation. Elemental analysis (SEM-EDS) is used on slag inclusion to estimate the initial iron and sword production processes. EPMA analysis was used to determine the carbon concentration throughout the samples, suggesting the use of wrought iron and hardening techniques. Results show that the Heythuysen sword contains multiple microstructure phases with various carbon concentrations. Most probably a combination of piling techniques and carburisation was applied during the production of the Heythuysen sword. The several bending methods of the replica show a distinction in the microstructure on the level of local misorientation. This is sensitive to the presence of inclusions and the changes in phase and grain size, which complicated the evaluation of the Heythuysen sword. The Heythuysen sword does not show strong evidence of bending by brute force and is most likely bent by a blacksmith with a fire.

Macro X-ray fluorescence (MA-XRF) is a recently developed technology allowing to obtain elemental information from cultural heritage objects. This information can, for example, be used to identify pigments used in a painting. Yet, the extended period of time it takes to scan an object is a major issue within MA-XRf. For instance, it took about 60 days to scan the Ghent Altarpiece. The long scanning time is a consequence of the necessary dwell time per pixel to create a robustly interpretable spectrum: the higher the dwell time, the higher the signalto-noise ratio (SNR), hence, the easier to detect elements. This thesis explores a possible solution for this problem using a denoising algorithm that increases the signal-to-noise ratio post-acquisition by exploiting the similarity between neighbouring pixels and spectra. To this end, a customized method of wavelet filter bank denoising is proposed. Current thresholding methods used in wavelet filter bank denoising are not suitable for filtering MA-XRF data, therefore, a novel thresholding method is introduced. Here, the widely used universal thresholding method is used as a basis, for which the formula for calculating the standard deviation of the detail coefficients of a channel is altered. Several design parameters of wavelet filter bank denoising were evaluated using a synthetic dataset, for which the performance quality indicators root mean square error (RMSE), mean absolute error (MAE) and SNR were determined. The parameters for which we optimized were the mother wavelet, the number of decomposition levels, and the number of neighbouring channels used for determining the standard deviation σ for thresholding. Good performance was obtained with the haar, db2, and coif1 wavelets, all at 3 levels of decomposition. A suitable number of neighbouring channels depended on the decomposition level and was determined to be 3 (on each side of the channel). Herewith, the signal-to-noise ratio was improved for both the average pixel spectra and the sum spectrum. The filtered synthetic dataset simulated to have a dwell time of 0.5 seconds had a SNR approximately equal to the raw synthetic dataset simulated to have a dwell time of 0.75 seconds. Hence, the algorithm succeeded in lowering the necessary dwell time. A case study of a daguerreotype was used to test the proposed denoising algorithm.

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.

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Retrieving actionable information from large datasets is increasingly computationally expensive due to the current trend of ever-increasing dataset sizes. Reducing dataset sizes with dimensionality reduction techniques is often necessary for statistical analysis techniques, such as classification, to be computationally feasible. Most dimensionality reduction methods do not require any additional information to accomplish their task. However, datasets used for classification, for example, are accompanied by a set of class-labels as well. This extra information can improve dimensionality reduction techniques by explicitly preserving features that explain differences between classes. A field where high-dimensional and large datasets are standard is Imaging Mass Spectrometry (IMS), a technique that simultaneously records the abundance and spatial location of molecules throughout biological tissue samples. Classification has been applied to IMS datasets for a wide range of scenarios, including the diagnosis of disease, distinguishing between tumour types for personalized treatment, and identifying biomarkers. A recently introduced dimensionality reduction method called Soft Discriminant Map (SDM), designed to incorporate class information and prevent overfitting when used on high-dimensional datasets, is a promising candidate to reduce the size and dimensionality of IMS datasets. However, SDM currently requires manual setting of a free parameter β that influences class separation in the newly constructed feature-space. This thesis explores the use of SDM on IMS datasets in classification use cases and proposes a framework to set β in a data-driven way: Data-Driven Soft Discriminant Map (DD-SDM). Furthermore, the sensitivity of the classification performance to changes in β is examined. DD-SDM is compared to similar state-of-the-art dimensionality reduction methods in terms of classification performance. The performed experiments show that DD-SDM successfully finds a value for β where the classification performance is on par with, or in some scenarios better than, state-of-the-art dimensionality reduction methods while using fewer features. Setting β either too low or too high results in a suboptimal feature space and worsens classification performance. Golden section search, the search strategy used to find the optimal β in DD-SDM, succeeds in finding the optimal β in fewer iterations than more naive methods. With the use of an artificial dataset in combination with a novel evaluation metric, the Peak Conservation Score (PCS), the distinctive ability of DD-SDM to discard features that are common between classes and to actively select for discriminative features is demonstrated. The DD-SDM framework is furthermore applied to real-world IMS measurements of rat brain and mouse kidney tissue.

As part of the NWO Science4Arts REVISRembrandt project (2012–2018), novel chemical imaging techniques were developed and applied to the study of Rembrandt’s late experimental painting technique (1651–1669). One of the unique features in his late paintings is his abundant use of smalt: a blue cobalt glass pigment that he often combined with organic lake pigments, earth pigments and blacks. Since most of these smalt-containing paints have discolored over time, we wanted to find out more about how these paintings may have originally looked, and what the role of smalt was in his paint. This paper reports on the use of smalt in complex pigment mixtures in Rembrandt’s Homer (1663), Mauritshuis, The Hague. Macroscopic X-ray fluorescence imaging (MA-XRF) assisted by computational analysis, in combination with SEM-EDX analysis of paint cross-sections, provides new information about the distribution and composition of the smalt paints in the painting. Paint reconstructions were carried out to investigate the effect of different percentages of smalt on the overall color, the drying properties, translucency and texture of the paint. Results show that the influence of (the originally blue) smalt on the intended color of the paint of the Homer is minimal. However, in mixtures with high percentages of smalt, or when combined with more transparent pigments, it was concluded that the smalt did produce a cooler and darker paint. It was also found that the admixture of opaque pigments reduced the translucent character of the smalt. The drying tests show that the paints with (cobalt-containing) smalt dried five times faster compared to those with glass (without cobalt). Most significantly, the texture of the paint was strongly influenced by adding smalt, creating a more irregular surface topography with clearly pronounced brushstrokes. Optical coherence tomography (OCT) was used as an additional tool to reveal differences in translucency and texture between the different paint reconstructions. In conclusion, this study confirmed earlier assumptions that Rembrandt used substantial amounts of smalt in his late paintings, not for its blue color, but to give volume and texture to his paints, to deepen their colors and to make them dry faster.

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Cultural heritage science envisages understanding of methods and techniques used by past painters and sculptors in creating their masterpieces of art. Existing devices for in situ and non-destructive, automated scanning are large and bulky and built around the assumption of a perfectly planar surface. We are developing a lightweight, portable robot for scanning of paintings, marbles, or statues while explicitly allowing for their out-of-plane surface. This paper presents the kinematic design and analysis of the wrench-feasible workspace of a cable-driven parallel robot capable of positioning an imaging device with three translational and two rotational degrees of freedom. At the end stand geometric parameters optimized for the application requirements allowing for pan and tilt of 70 each in total, making scanning of the spatial surface of art objects possible.

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Caspar David Friedrich (1774–1840), one of the most famous German 19th c. painters, created paintings throughout his artistic life using different paint palettes, including many new pigments from the turn from the 18th to the 19th century. In that regard especially blue and yellow pigments are the focus of this non-invasive chemical study using X-ray fluorescence imaging, as these are a landscape painter's major colours. Four paintings from the collection of the Alte Nationalgalerie, Staatliche Museen zu Berlin-Stiftung Preußischer Kulturbesitz, spanning over two important decades of Friedrich's artistic career, were investigated in situ to determine the chemical composition of the blue pigments used in the sky and the yellow hues used in the sunsets and moonlight. The results indicate the use of iron based yellow pigments as well as smalt based blue pigments in Friedrich's early works, while chromium-based yellow pigments and cobalt blue are used in later paintings. The finding of cadmium sulphide in a painting dated in 1817, probably as a historical retouching, is interesting and requires further research. This in situ non-invasive imaging study, although limited to one analytical technique, shows Friedrich's introduction of new synthetic pigments into his paint palette, which varies over the time. These results are important to better understand the painting technique of Caspar David Friedrich and his contemporaries.

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The scientific analysis of historical paintings has been traditionally restricted to the analysis of paint cross-section samples. This invasive method provides extensive information but is inherently limited in scope due to the extreme heterogeneity of paintings. In the last decade, non-invasive spot analyses and spectral imaging methods have become increasingly widespread in cultural heritage science. Two of these methods are macroscopic X-ray fluorescence imaging spectroscopy (MA-XRF) and reflectance imaging spectroscopy (RIS). These methods allow for 2D-scanning the entire surface of a painting and provide complementary information on elemental and molecular composition and distribution of the paint. However, these methods are often used only for qualitative analysis of the paint based on relative distribution maps, revealing only limited information about the paint layer stratigraphy. This thesis is an exploration of a combined approach for quantitative analysis of paint composition and layer stratigraphy using MA-XRF and RIS. The research used a set of specially prepared paint samples of mixtures and multiple layer applications based on historically relevant pigments which were scanned using MA-XRF and RIS in the visible and Near IR range (400-2500 nm). The spectral data acquired were processed and analyzed in a variety of ways, including Non-negative Matrix Factorization, Non-Linear Least Square Fitting, among other methods; in an attempt to gather quantitative compositional and stratigraphic data. In these trials, using characteristic reflectance features in the visible range together with a comparison of highly and lightly absorbed X-ray fluorescence lines allowed for the identification and quantification of surface specific compounds related to the top paint layer. Further comparison of fluorescence lines and absorption features in the Near IR range provided a potential avenue for quantification of subsurface paint layers. The results confirm that the combination of these methods allows to reveal the paint stratigraphy. The project provides samples and data sets which may serve as the basis for the development of a robust algorithm to address this issue in the future.