Reworking paintings has been a common practice throughout art history, with artists modifying their own work at various stages and occasionally altering pieces by others. Advances in chemical imaging and increased access to traditional imaging techniques have facilitated the documentation of such interventions. Initially focused on Old Masters, research on reworking practices has expanded to nineteenth- and twentieth-century artists. For the first time, a classification system for reworkings is introduced, based on the oeuvre of Belgian Modernist painter James Ensor (1860–1949). Five representative case studies each illustrate one of the proposed types of reworking: (1) pentimenti, (2) post-factum revisions, (3) recycled works, (4) metamorphoses, and (5) appropriations. Using advanced imaging and spectroscopic techniques, including Macro X-Ray Fluorescence (MA-XRF) and instrumentation from the Iperion HS consortium, the study also provides material-technical evidence of Ensor’s experimental studio practice while shedding new light on anachronisms in his oeuvre.

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.

The blue pigment smalt, a synthetic potash glass tinted with cobalt, was widely used between the sixteenth and the eighteenth centuries. As part of a study on the alteration of smalt and the reconstitution of its original color, the painting: Woman doing a Libation or Artemisia (Fontainebleau school, 1570) was examined in which the artist used smalt as a blue pigment, which is now degraded. Noninvasive imaging was performed using macro-2D X-ray fluorescence and reflectance imaging spectroscopy to get an overview of the artist’s palette and its distribution. Samples prepared as cross sections were also analyzed by scanning electron microscopy coupled with energy dispersive spectroscopy, micro-X-ray absorption near-edge structure spectroscopy and synchrotron micro-X-ray diffraction imaging to determine the preservation state of the smalt as well as structural information on other pigments adjacent to smalt grains in individual paint layers, which could play a role in the degradation process. On the one hand, the study conducted on the alteration of smalt has shown that it is very weathered and mixed with hydrocerussite, which could be a factor that would facilitate the alteration. On the other hand, these analyses have made it possible to identify and locate the pigments used, which will be the basis for the virtual reconstruction of the color of the painting.

Scanning electron microscopy coupled with microanalysis (SEM-EDX) is an important analytical tool for the morphological and chemical characterization of different types of materials. In many applications, SEM-EDX elemental maps are usually used and processed as images, thus flattening and reducing the spectroscopic information contained in EDX hyperspectral data cubes. The exploitation of the full hyperspectral dataset could be indeed very useful for the study of complex matrices like soil. In order to maximize the information attainable by SEM-EDX data cubes analysis, the software package “Datamuncher Gamma” was implemented and applied to study soil aggregates. By using this approach, different phases (silicates, aluminosilicates, Ca-carbonates, Ca-phosphates, organic matter, iron oxides) inside soil aggregates were successfully identified and segmented. The advantages of this method over the common ROI imaging approach are presented. Finally, this method was used to compare different aggregates in a Cr-polluted soil and understand their possible pedological history. The present method can be used for the analysis of every type of SEM-EDX data cubes, allowing its application to different types of samples and fields of study.

X-ray fluorescence spectroscopy (XRF) plays an important role for elemental analysis in a wide range of scientific fields, especially in cultural heritage. XRF imaging, which uses a raster scan to acquire spectra pixel-wise across artworks, provides the opportunity for spatial analysis of pigment distributions based on their elemental composition. However, conventional XRF-based pigment identification relies on time-consuming elemental mapping facilitated by the interpretation of measured spectra by experts. To reduce the reliance on manual work, recent studies have applied machine learning techniques to cluster similar XRF spectra in data analysis and to identify the most likely pigments. Nevertheless, it is still challenging to implement automatic pigment identification strategies to directly tackle the complex structure of real paintings, e.g. pigment mixtures and layered pigments. In addition, pigment identification based on XRF on a pixel-by-pixel basis remains an obstacle due to the high noise level. Therefore, we developed a deep-learning based pigment identification framework to fully automate the process. In particular, this method offers high sensitivity to the underlying pigments and to the pigments present in low concentrations, therefore enabling robust mapping of pigments based on single-pixel XRF spectra. As case studies, we applied our framework to lab-prepared mock-up paintings and two 19th-century paintings: Paul Gauguin's Poèmes Barbares (1896) that contains layered pigments with an underlying painting, and Paul Cezanne's The Bathers (1899-1904). The pigment identification results demonstrated that our model achieved comparable results to the analysis by elemental mapping, suggesting the generalizability and stability of our model.

If ancient written sources and the visual analysis of polychromies have recently revealed the complexity of the technique of painting on statues and their frequent restoration, the non-invasive punctual chemical analyses carried out do not allow one to access the chemical composition of the different paint layers. This paper presents the analysis of three statues from Roman Africa discussing the results obtained from this understudied territory and chronology. By combining visual observation (VIS, UVL), video microscopy and MA-XRF imaging, we propose here a non-invasive protocol to determine the chemical composition of the different paint layers. This allows one to unveil the complexity of the ‘know-how’ of a sculpture painter and sheds light on the evolution of the original appearance of the statues.

X-ray fluorescence (XRF) spectroscopy is a common technique in the field of heritage science. However, data processing and data interpretation remain a challenge as they are time consuming and often require a priori knowledge of the composition of the materials present in the analyzed objects. For this reason, we developed an open-source, unsupervised dictionary learning algorithm reducing the complexity of large datasets containing 10s of thousands of spectra and identifying patterns. The algorithm runs in Julia, a programming language that allows for faster data processing compared to Python and R. This approach quickly reduces the number of variables and creates correlated elemental maps, characteristic for pigments containing various elements or for pigment mixtures. This alternative approach creates an overcomplete dictionary which is learned from the input data itself, therefore reducing the a priori user knowledge. The feasibility of this method was first confirmed by applying it to a mock-up board containing various known pigment mixtures. The algorithm was then applied to a macro XRF (MA-XRF) data set obtained on an 18th century Mexican painting, and positively identified smalt (pigment characterized by the co-occurrence of cobalt, arsenic, bismuth, nickel, and potassium), mixtures of vermilion and lead white, and two complex conservation materials/interventions. Moreover, the algorithm identified correlated elements that were not identified using the traditional elemental maps approach without image processing. This approach proved very useful as it yielded the same conclusions as the traditional elemental maps approach followed by elemental maps comparison but with a much faster data processing time. Furthermore, no image processing or user manipulation was required to understand elemental correlation. This open-source, open-access, and thus freely available code running in a platform allowing faster processing and larger data sets represents a useful resource to understand better the pigments and mixtures used in historical paintings and their possible various conservation campaigns.

A scrapbook compiled between 1660 and 1687 by Gesina ter Borch (1631–1690), a female artist from the small town of Zwolle in the Netherlands, contains an intriguing painting on paper of a full-length portrait of a young Iranian. Although the figure wears the attributes in vogue at the Safavid court of Isfahan, certain elements seem rather incongruous and peculiar. The general composition appears static and rigid, an impression reinforced by an unusual black painted background. Stylistic differences within the painting were also observed, hinting at alterations to the original painting. To investigate the history of the painting and to reconstruct the original composition and identify the later additions, perhaps made by Gesina herself, the painting was examined with different imaging and analytic techniques available at the Conservation and Science Department of the Rijksmuseum. This allowed the research team to discriminate between pigments used for the original composition and pigments used to conceal damaged areas of the painting and added pictorial elements. After interpreting scientific results, as well as historical findings, it was possible to shed light on the use of specific pigments, namely lead white and smalt, and on the possible misinterpretation of some details, such as the cup held by the young man. The results of macro X-ray fluorescence scanning (MA-XRF) and lead isotope analysis, viewed in the light of information about the economic and cultural exchanges between Iran and the Netherlands in the seventeenth century, fed new theories about the origin and history of this painting. The painting, originally made in Iran in the style of Riza Abbasi, the head of the Emperor Shah Abbas’ library, ended up in Gesina ter Borch's workshop and may have been ‘restored’ by the artist to improve its condition and to match her tastes.

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.

The nonplanar shape of a painting as well as practical constraints often result in the painting's surface not being parallel to the plane in that the measurement head of a MA-XRF scanner is being moved. Changing the working distance affects the measurement geometry, so that the sensitivity for the same element may vary throughout the investigated area and induce visible artifacts. These artifacts are especially visible when different scans of the same painting are stitched together. In this article, we present an approach to correct for the variation of the measurement distance. We explored using an intrinsic part of the XRF data set, the Ar signal from the air, to estimate the distance between surface and instrument. The model is developed based on fundamental parameter calculations and a measurement of a NIST 610 standard and is verified on a set of scans of Rembrandt's ‘Portrait of Oopjen Coppit (1611–1689)’.

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.