Neural network-based classification of X-ray fluorescence spectra of artists' pigments: an approach leveraging a synthetic dataset created using the fundamental parameters method

X-ray fluorescence (XRF) spectroscopy is an analytical technique used to identify chemical elements that has found widespread use in the cultural heritage sector to characterise artists' materials including the pigments in paintings. It generates a spectrum with characteristic emission lines relating to the elements present, which is interpreted by an expert to understand the materials therein. Convolutional neural networks (CNNs) are an effective method for automating such classification tasks—an increasingly important feature as XRF datasets continue to grow in size—but they require large libraries that capture the natural variation of each class for training. As an alternative to having to acquire such a large library of XRF spectra of artists' materials a physical model, the Fundamental Parameters (FP) method, was used to generate a synthetic dataset of XRF spectra representative of pigments typically encountered in Renaissance paintings that could then be used to train a neural network. The synthetic spectra generated—modelled as single layers of individual pigments—had characteristic element lines closely matching those found in real XRF spectra. However, as the method did not incorporate effects from the X-ray source, the synthetic spectra lacked the continuum and Rayleigh and Compton scatter peaks. Nevertheless, the network trained on the synthetic dataset achieved 100% accuracy when tested on synthetic XRF data. Whilst this initial network only attained 55% accuracy when tested on real XRF spectra obtained from reference samples, applying transfer learning using a small quantity of such real XRF spectra increased the accuracy to 96%. Due to these promising results, the network was also tested on select data acquired during macro XRF (MA-XRF) scanning of a painting to challenge the model with noisier spectra Although only tested on spectra from relatively simple paint passages, the results obtained suggest that the FP method can be used to create accurate synthetic XRF spectra of individual artists' pigments, free from X-ray tube effects, on which a classification model could be trained for application to real XRF data and that the method has potential to be extended to deal with more complex paint mixtures and stratigraphies

Image Separation with Side Information: A Connected Auto-Encoders Based Approach

X-radiography (X-ray imaging) is a widely used imaging technique in art investigation. It can provide information about the condition of a painting as well as insights into an artist’s techniques and working methods, often revealing hidden information invisible to the naked eye. X-radiograpy of double-sided paintings results in a mixed X-ray image and this paper deals with the problem of separating this mixed image. Using the visible color images (RGB images) from each side of the painting, we propose a new Neural Network architecture, based upon ’connected’ auto-encoders, designed to separate the mixed X-ray image into two simulated X-ray images corresponding to each side. This connected auto-encoders architecture is such that the encoders are based on convolutional learned iterative shrinkage thresholding algorithms (CLISTA) designed using algorithm unrolling techniques, whereas the decoders consist of simple linear convolutional layers; the encoders extract sparse codes from the visible image of the front and rear paintings and mixed X-ray image, whereas the decoders reproduce both the original RGB images and the mixed X-ray image. The learning algorithm operates in a totally self-supervised fashion without requiring a sample set that contains both the mixed X-ray images and the separated ones. The methodology was tested on images from the double-sided wing panels of the Ghent Altarpiece , painted in 1432 by the brothers Hubert and Jan van Eyck. These tests show that the proposed approach outperforms other state-of-the-art X-ray image separation methods for art investigation applications

Mixed X-Ray Image Separation for Artworks with Concealed Designs

In this paper, we focus on X-ray images of paintings with concealed sub-surface designs (e.g., deriving from reuse of the painting support or revision of a composition by the artist), which include contributions from both the surface painting and the concealed features. In particular, we propose a self-supervised deep learning-based image separation approach that can be applied to the X-ray images from such paintings to separate them into two hypothetical X-ray images. One of these reconstructed images is related to the X-ray image of the concealed painting, while the second one contains only information related to the X-ray of the visible painting. The proposed separation network consists of two components: the analysis and the synthesis sub-networks. The analysis sub-network is based on learned coupled iterative shrinkage thresholding algorithms (LCISTA) designed using algorithm unrolling techniques, and the synthesis sub-network consists of several linear mappings. The learning algorithm operates in a totally self-supervised fashion without requiring a sample set that contains both the mixed X-ray images and the separated ones. The proposed method is demonstrated on a real painting with concealed content, Do\~na Isabel de Porcel by Francisco de Goya, to show its effectiveness

A Learning Based Approach to Separate Mixed X-Ray Images Associated with Artwork with Concealed Designs

X-ray images are widely used in the study of paintings. When a painting has hidden sub-surface features (e.g., reuse of the canvas or revision of a composition by the artist), the resulting X-ray images can be hard to interpret as they include contributions from both the surface painting and the hidden design. In this paper we propose a self-supervised deep learning-based image separation approach that can be applied to the X-ray images from such paintings (‘mixed X-ray images’) to separate them into two hypothetical X-ray images, one containing information related to the visible painting only and the other containing the hidden features. The proposed approach involves two steps: (1) separation of the mixed X-ray image into two images, guided by the combined use of a reconstruction and an exclusion loss; (2) even allocation of the error map into the two individual, separated X-ray images, yielding separation results that have an appearance that is more familiar in relation to Xray images. The proposed method was demonstrated on a real painting with hidden content, Doña Isabel de Porcel by Francisco de Goya, to show its effectiveness

Mixed X-Ray Image Separation for Artworks with Concealed Designs

In this paper, we focus on X-ray images (Xradiographs) of paintings with concealed sub-surface designs (e.g., deriving from reuse of the painting support or revision of a composition by the artist), which therefore include contributions from both the surface painting and the concealed features. In particular, we propose a self-supervised deep learning-based image separation approach that can be applied to the Xray images from such paintings to separate them into two hypothetical X-ray images. One of these reconstructed images is related to the X-ray image of the concealed painting, while the second one contains only information related to the X-ray image of the visible painting. The proposed separation network consists of two components: the analysis and the synthesis sub-networks. The analysis sub-network is based on learned coupled iterative shrinkage thresholding algorithms (LCISTA) designed using algorithm unrolling techniques, and the synthesis sub-network consists of several linear mappings. The learning algorithm operates in a totally self-supervised fashion without requiring a sample set that contains both the mixed X-ray images and the separated ones. The proposed method is demonstrated on a real painting with concealed content, Dona Isabel de Porcel ˜ by Francisco de Goya, to show its effectiveness

Non-invasive multiresidue screening methods for the determination of pesticides in heritage collections

This paper describes the development of a novel non-invasive sampling and analysis method that can be used to assess the presence of volatile pesticides on objects held in heritage collections. Vapour phase sampling was conducted using sampling tubes loaded with Tenax-TA™ and trapped analytes were determined using thermal desorption-gas chromatography–mass spectrometry (TD-GC-MS). The results of this study are presented in a simple ‘decision tree’ diagram to provide the heritage sector with the best methods to identify the presence of pesticides in collections. To illustrate the use of the methodology developed, the results from two case studies in heritage institutions are presented. Attempts were made to measure a range of pesticides, known to have been used in heritage collections, in the vapour phase including aldrin, camphor, chloronaphthalene, dichlorodiphenyltrichloroethane (4,4′-DDT), dichlorvos, dieldrin, endrin, a mixture of α-, β-, γ- and δ-hexachlorocyclohexane (hereafter referred to as HCH), naphthalene, and thymol. Of the analytes included in this study, as expected 4,4′-DDT was not sufficiently volatile to be detected in the vapour phase and swab sampling (using hexane) is recommended for this analyte. After method development and validation, the air inside a display case (Swiss Cottage, Isle of Wight) was sampled. The results gave a positive identification for camphor, chloronaphthalene and naphthalene. In contrast, the air around a ceremonial dance mask from the British Museum was analysed but no volatile pesticides were identified. In this case, liquid chromatographic analysis of swab samples from the mask yielded a positive identification of dichlorvos. The proposed non-invasive sampling methods require sampling of a volume of air around an object. To be detected the pesticide must possess suitable volatility. It was demonstrated that camphor, chloronaphthalene, naphthalene and thymol could be successfully trapped onto Tenax TA™ sorbent tubes and pseudo-quantitatively analysed using TD-GC-MS. Dichlorvos, HCH, aldrin, dieldrin and endrin were also trapped onto Tenax TA™ and qualitatively detected by TD-GC-MS. Although a key objective of the developed methods was non-invasive sampling, the low volatility of 4,4′-DDT precluded it from vapour phase monitoring and hexane swabbing followed by HPLC analysis was required

Emission profiles from polymeric materials : characterised by thermal desorption-gas chromatography

Since being recognised as a potential emissive source, plastics in heritage collections are being investigated to understand the chemical compounds they release and how they might affect the stability of other heritage objects. There is a requirement for non-invasive methods of analyses to identify unknown plastics and the emitted volatiles they generate. Therefore, Tenax-TA sampling tubes were used to collect the emitted volatiles from 41 samples of 9 polymer types of varying formulation, provenance and age. Thermal desorption-gas chromatography coupled with mass spectrometry (TD-GC/MS) has been successfully used to separate and identify the emissions of the 41 samples at 23 C, after heating to 70 C and after accelerated degradation

Technological insights into madder pigment production in antiquity

International audienc

Assessing the potential of historic archaeological collections: a pilot study of the British Museum’s Swiss lake village textiles

The British Museum houses a significant collection of organic material from prehistoric Swiss Lakes Villages (c.4000 BC to 500 BC) excavated in the late nineteenth century. The unusual waterlogged, anaerobic, alkaline burial environment provides suitable preservational conditions for a range of organic materials including many textiles. The textiles, which include fine complex weaves, netting and skeins, remain as treated at that time, mounted between sheets of glass or in vials but are now at risk due to acidic mounting materials, broken glass, or the fragments being insecure in their frames. The condition of the textile collection has been assessed with a view to improving the storage and display of these rare survivals, while preserving the original and highly informative historic mounts. Although often poorly provenanced, the opportunity was also taken to assess the potential of the textile collection for detailed study, including fibre identification and weave analysis. Many of the textiles showed signs of early consolidation and the presence of detrital material but it was possible to identify a range of plant materials in all samples examined. The consolidant used on OA.10836 has been identified as a carbohydrate material, possibly a gum or sugar solution. Although there was no clear visual evidence for the use of organic dyes, the chemical condition of fibres in OA.10836 suggests that no evidence would now remain