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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. 

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. 

Cultural heritage materials, ranging from archaeological objects and sites to fine arts collections, are often characterized through their life cycle. In this review, the fundamentals and tools of materials science are used to explore such life cycles—first, via the origins of the materials and methods used to produce objects of function and artistry, and in some cases, examples of exceptional durability. The findings provide a window on our cultural heritage. Further, they inspire the design of sustainable materials for future generations. Also explored in this review are alteration phenomena over intervals as long as millennia or as brief as decades. Understanding the chemical processes that give rise to corrosion, passivation, or other degradation in chemical and physical properties can provide the foundation for conservation treatments. Finally, examples of characterization techniques that have been invented or enhanced to afford studies of cultural heritage materials, often nondestructively, are highlighted. 

While the chemistry of artists’ paints has previously been studied and reviewed, these studies only capture a portion of the properties affecting the response of paint materials. The mechanical properties of artists’ paints relate to the deformation response of these materials when a stress is applied. This response is dependent on many factors, such as paint composition, pigment to binder ratio, temperature, relative humidity, and solvent exposure. Here, thirty years of tensile testing data have been compiled into a single dataset, along with the testing conditions, to provide future researchers with easy access to these data as well some general discussion of their trends. Alongside the more commonly used techniques of tensile testing and dynamic mechanical analysis, new techniques have been developed to more fully investigate the mechanical properties, and are discussed along with salient results. The techniques have been divided into two categories: those that are restricted to use on model systems and those that are applicable to historic samples. Techniques applied to model systems (tensile testing, dynamic mechanic analysis, quartz crystal microbalance, vibration studies) require too large of a sample to be taken from art objects or focus on the mechanical properties of the liquid state (shear rheometry). Techniques applied to historic samples incorporate the use of small sample sizes (nanoindentation), optical techniques (laser shearography), computational simulations (finite element analysis), and non-invasive comparative mechanical properties (single-sided nuclear magnetic resonance) to investigate and predict the mechanical properties of paints.

 

We introduce a system that exploits the screen and front-facing camera of a mobile device to perform three-dimensional deflectometry-based surface measurements. In contrast to current mobile deflectometry systems, our method can capture surfaces with large normal variation and wide field of view (FoV). We achieve this by applying automated multi-view panoramic stitching algorithms to produce a large FoV normal map from a hand-guided capture process without the need for external tracking systems, like robot arms or fiducials. The presented work enables 3D surface measurements of specular objects ’in the wild’ with a system accessible to users with little to no technical imaging experience. We demonstrate high-quality 3D surface measurements without the need for a calibration procedure. We provide experimental results with our prototype Deflectometry system and discuss applications for computer vision tasks such as object detection and recognition.