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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 Poεave;mes 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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The pigment use in the Tomb of the Reliefs (fourth century BC) and four other tombs (seventh to fourth century BC) in the Etruscan Banditaccia Necropolis near Cerveteri, Italy, has been investigated. We made use of X‐ray fluorescence (XRF) and Raman spot analysis, XRF imaging, infrared luminescence and photography supported by Dstretch contrast enhancement. We identified the use of haematite, goethite, calcite, carbon black, manganese oxide, Tyrian purple, and Egyptian blue, with the last three only found under the central burial place of the Tomb of the Reliefs. The tuff in which these tombs were cut proved to be a significant challenge for XRF analysis as the signals of Ca, Mn, and Fe vary strongly due to the heterogeneity of the stone, so that it is difficult to distinguish between signals from the tuff and the pigments. Finally, we show that lightweight instruments transported in check‐in luggage may not answer all questions on pigment use but do provide additional insights. [ABSTRACT FROM AUTHOR]

Abstract Hyperspectral imaging allows the acquisition of representative information on cultural heritage objects. For a complete identification of the chemical compounds present, one needs instruments employing different forms of radiation, sensitive to different chemical features. Data treatment workflows are commonly developed for a single spectroscopic technique, so that data sets are evaluated separately, and the reduced representations yielded are compared during interpretation. In this publication, we describe how t-stochastic neighbor embedding (t-SNE) allows for the evaluation and interpretation of data acquired in a Ramesside tomb (13th century BC) of the Theban Necropolis in Egypt. By fusing X-ray Fluorescence (XRF) imaging and Reflectance Imaging Spectroscopy (RIS) into a single data set, we exploit the synergies between both methods to support interpretation and presentation of the results yielded. [ABSTRACT FROM AUTHOR]

Technological progress allows for an ever-faster acquisition of hyperspectral data, challenging the users to keep up with interpreting the recorded data. Matrix factorization, the representation of data sets by bases (or loads) and coefficient (or score) images is long used to support the interpretation of complex data sets. We propose in this publication Simplex Volume Maximization (SiVM) for the analysis of X-ray fluorescence (XRF) imaging data sets. SiVM selects archetypical data points that represents the data set and thus provides easily understandable bases, preserves the non-negative character of XRF data sets and has low demands concerning computing resources. We apply SiVM on an XRF data set of Hans Memling's Portrait of a man from the Lespinette family from the collection of the Mauritshuis (The Hague, NL) and discuss capabilities and shortcomings of SiVM. [ABSTRACT FROM AUTHOR]

The high brilliance of third-generation synchrotron sources increases the demand for faster detectors to utilize the available flux. The Maia detector is an advanced imaging scheme for energy-dispersive detection realising dwell times per image-pixel as low as 50 µs and count rates higher than 10 × 106 s−1. In this article the integration of such a Maia detector in the Microprobe setup of beamline P06 at the storage ring PETRA III at the Deutsches Elektronen-Synchrotron (DESY) in Hamburg, Germany, is described. The analytical performance of the complete system in terms of rate-dependent energy resolution, scanning-speed-dependent spatial resolution and lower limits of detection is characterized. The potential of the Maia-based setup is demonstrated by key applications from materials science and chemistry, as well as environmental science with geological applications and biological questions that have been investigated at the P06 beamline. [ABSTRACT FROM AUTHOR]

Elemental distribution images acquired by imaging X-ray fluorescence analysis can contain high degrees of redundancy and weakly discernible correlations. In this article near real-time non-negative matrix factorization (NMF) is described for the analysis of a number of data sets acquired from samples of a bi-modal α+β Ti-6Al-6V-2Sn alloy. NMF was used for the first time to reveal absorption artefacts in the elemental distribution images of the samples, where two phases of the alloy, namely α and β, were in superposition. The findings and interpretation of the NMF results were confirmed by Monte Carlo simulation of the layered alloy system. Furthermore, it is shown how the simultaneous factorization of several stacks of elemental distribution images provides uniform basis vectors and consequently simplifies the interpretation of the representation. [ABSTRACT FROM AUTHOR]

Imaging methods with elemental contrast are of great value for the investigation of historical paintings, as they allow for study of sub-surface layers that provide insight into a painting's creation process. Two of the most important methods are neutron activation autoradiography (NAAR) and scanning macro-XRF (MA-XRF). Given the differences between these methods in the fundamental physical phenomena exploited, a theoretical comparison of their capabilities is difficult and until now a critical comparison of their use on the same painting is missing. In this paper, we present a study of Rembrandt van Rijn's painting Susanna and the Elders from the Gemäldegalerie in Berlin employing both techniques. The painting features a considerable number of overpainted features and a wide range of pigments with different elemental tracers, including earth pigments (Mn/Fe), Azurite (Cu), lead white (Pb), vermilion (Hg) and smalt (Co, As). MA-XRF can detect all elements above Si ( Z = 14), suffers from few spectral overlaps and can be performed in a few tens of hours in situ, i.e. in a museum. NAAR requires the stay of the painting at a research facility for several weeks, and inter-element interferences can be difficult to resolve. Also, only a limited number of elements contribute to the acquired autoradiographs, most notably Mn, Cu, As, Co, Hg and P. However, NAAR provides a higher lateral resolution and is less hindered by absorption in covering layers, which makes it the only method capable of visualizing P in lower paint layers. [ABSTRACT FROM AUTHOR]

Technical progress in the fields of X-ray sources, optics and detectors is constantly enhancing the pace of data acquisition in XRF imaging. This enlarges the size of the hyperspectral datasets and the number of their sub-parts. This paper describes the challenges in processing large XRF datasets featuring several million pixels/spectra and the strategies developed to overcome them. During the investigation of historical paintings by scanning macro-XRF the main challenges are the correct identification of all spectral features in a dataset and its timely processing. For the identification of spectral features different approaches are discussed, i.e. the use of sum spectra, maximum pixel spectra and of χr2 maps. For the time-efficient, artefact-free evaluation of XRF imaging data, different software packages are evaluated and intercompared (AXIL, PyMCA, GeoPIXE and the in-house written datamuncher). The process of data evaluation is illustrated on a large dataset (3.4 MPixels) acquired during the investigation of a version of Caravaggio's Supper at Emmaus (143 × 199.5 cm2). This 17th century painting is currently the largest object entirely scanned with macroscopic XRF. [ABSTRACT FROM AUTHOR]

Abstract: Hidden, sub-surface paint layers and features contain valuable information for the art-historical investigation of a painting's past and for its conservation for coming generations. The number of techniques available for the study of these features has been considerably extended in the last decades and established techniques have been refined. This review focuses on mobile non-destructive subsurface imaging and depth profiling techniques, which allow for the in-situ investigation of easel paintings, i.e. paintings on a portable support. Among the techniques discussed are: X-ray radiography and infrared reflectography, which are long established methods and are in use for several decades. Their capabilities of element/species specific imaging have been extended by the introduction of energy/wavelength resolved measurements. Scanning macro-X-ray fluorescence analysis made it for the first time possible to acquire elemental distribution images in-situ and optical coherence tomography allows for the non-destructive study the surface paint layers in virtual cross-sections. These techniques and their variants are presented next to other techniques, such as Terahertz imaging, Nuclear Magnetic Resonance depth profiling and established techniques for non destructive testing (thermography, ultrasonic imaging and laser based interference methods) applied in the conservation of historical paintings. Next to selected case studies the capabilities and limitations of the techniques are discussed. [Copyright &y& Elsevier]

In this study, P and other low Z elements were determined in aerosol particulates from ambient air using synchrotron radiation-induced total-reflection X-ray fluorescence analysis. Atmospheric transport of nutrients such as P or Si to the oceans is a key factor to marine plankton growth. Concentration of these elements in marine air masses is generally low (P < 10 ng/m3). Therefore, analytical procedures enabling for low detection limits are of interest. Because particle size is strongly correlated to its origin and sedimentation, the aerosols were collected with the aid of a low-pressure Berner impactor, which separates the aerosol particulates in nine size fractions with the smallest fraction from 15 to 30 nm. To be able to determine low Z elements, measurements were performed under vacuum conditions at the FLUO beamline at the ANKA synchrotron. An excitation energy of 3.5 keV near the P K-edge but below the Ca K absorption edge was chosen to avoid interferences of the P K-line with the detector escape artifact of the Ca-K α line. The result showed P was present in concentration from 2 to 180 ng/m3. Detection limits were found to be generally 0.2-0.3 ng/m3 for a collecting time of 1 h for the aerosols, which is an improvement to detection limits reported in other studies. Other elements such as S, Cl, and Si were determined as well. Si like P is a nutrient for marine plankton (diatoms). S and Cl play an important role in cloud formation, e.g. polar stratospheric clouds. Copyright © 2013 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Since the recent development of a mobile setup, MA-XRF scanning proved a valuable tool for the non-invasive, technical study of paintings. In this work, the applicability of MA-XRF scanning for investigating stained-glass windows inside a conservation studio is assessed by analysis of a high-profile, well-studied late-mediaeval panel. Although accurate quantification of components is not feasible with this analytical imaging technique, plotting the detected intensities of K versus Ca in a scatter plot allowed distinguishing glass fragments of different compositional types within the same panel. In particular, clusters in the Ca/K correlation plot revealed the presence of two subtypes of potash glass and three subtypes of high lime low alkali glass. MA-XRF results proved consistent with previous quantitative SEM-EDX analysis on two samples and analytical-based theories on glass production in the Low Countries formulated in literature. A bi-plot of the intensities of the more energetic Rb–K versus Sr–K emission lines yielded a similar glass type differentiation and is here presented as suitable alternative in case the Ca/K signal ratio is affected by superimposed weathering crusts. Apart from identification of the chromophores responsible for the green, blue and red glass colors, contrasting the associated elemental distribution maps obtained on the exterior and interior side of the glass permitted discriminating between colored pot metal glass and multi-layered flashed glass as well. Finally, the benefit of obtaining compositional information from the entire surface, as opposed to point analysis, was illustrated by the discovery of what appears to be a green cobalt glass—a feature that was previously missed on this well-studied stained-glass window, both by connoisseurs and spectroscopic sample analysis. [ABSTRACT FROM AUTHOR]