An official website of the United States government
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.
more »
« less
XRFast a new software package for processing of MA-XRF datasets using machine learning
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.
more »
« less
- Award ID(s):
- 1743748
- PAR ID:
- 10443687
- Date Published:
- Journal Name:
- Journal of Analytical Atomic Spectrometry
- Volume:
- 37
- Issue:
- 10
- ISSN:
- 0267-9477
- Page Range / eLocation ID:
- 2130 to 2143
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Carbon-based black pigments, a widely used class of pigments, are difficult to differentiate with the noninvasive techniques currently used in cultural heritage science. We use pump-probe microscopy, coupled with a support vector machine, to distinguish common carbon-based black pigments as pure pigments, as two-component black pigment mixtures, and as a mixture of a black and a colorful pigment. This work showcases the potential of pump-probe microscopy to spatially differentiate carbon-based black pigments, which would have interesting applications to works of art.more » « less
-
Flowers have evolved remarkable diversity in petal color, in large part due to pollinator-mediated selection. This diversity arises from specialized metabolic pathways that generate conspicuous pigments. Despite the clear link between flower color and floral pigment production, studies determining predictive relationships between pigmentation and petal color are currently lacking. In this study, we analyze a dataset consisting of hundreds of natural Penstemon hybrids that exhibit variation in flower color, including blue, purple, pink, and red. For each individual hybrid, we measured anthocyanin pigment content and petal spectral reflectance. We found that floral pigment quantities are correlated with hue, chroma, and brightness as calculated from petal spectral reflectance data: hue is related to the relative amounts of delphinidin vs. pelargonidin pigmentation, whereas brightness and chroma are correlated with the total anthocyanin pigmentation. We used a partial least squares regression approach to identify predictive relationships between pigment production and petal reflectance. We find that pigment quantity data provide robust predictions of petal reflectance, confirming a pervasive assumption that differences in pigmentation should predictably influence flower color. Moreover, we find that reflectance data enables accurate inferences of pigment quantities, where the full reflectance spectra provide much more accurate inference of pigment quantities than spectral attributes (brightness, chroma, and hue). Our predictive framework provides readily interpretable model coefficients relating spectral attributes of petal reflectance to underlying pigment quantities. These relationships represent key links between genetic changes affecting anthocyanin production and ecological functions of petal coloration.more » « less
-
Synopsis Flowers have evolved remarkable diversity in petal color, in large part due to pollinator-mediated selection. This diversity arises from specialized metabolic pathways that generate conspicuous pigments. Despite the clear link between flower color and floral pigment production, quantitative models inferring predictive relationships between pigmentation and reflectance spectra have not been reported. In this study, we analyze a dataset consisting of hundreds of natural Penstemon hybrids that exhibit variation in flower color, including blue, purple, pink, and red. For each individual hybrid, we measured anthocyanin pigment content and petal spectral reflectance. We found that floral pigment quantities are correlated with hue, chroma, and brightness as calculated from petal spectral reflectance data: hue is related to the relative amounts of delphinidin vs. pelargonidin pigmentation, whereas brightness and chroma are correlated with the total anthocyanin pigmentation. We used a partial least squares regression approach to identify predictive relationships between pigment production and petal reflectance. We find that pigment quantity data provide robust predictions of petal reflectance, confirming a pervasive assumption that differences in pigmentation should predictably influence flower color. Moreover, we find that reflectance data enables accurate inferences of pigment quantities, where the full reflectance spectra provide much more accurate inference of pigment quantities than spectral attributes (brightness, chroma, and hue). Our predictive framework provides readily interpretable model coefficients relating spectral attributes of petal reflectance to underlying pigment quantities. These relationships represent key links between genetic changes affecting anthocyanin production and the ecological functions of petal coloration.more » « less
-
Non-adiabatic vibrational-electronic resonance in the excited electronic states of natural photosynthetic antennas drastically alters the adiabatic framework, in which electronic energy transfer has been conventionally studied, and suggests the possibility of exploiting non-adiabatic dynamics for directed energy transfer. Here, a generalized dimer model incorporates asymmetries between pigments, coupling to the environment, and the doubly excited state relevant for nonlinear spectroscopy. For this generalized dimer model, the vibrational tuning vector that drives energy transfer is derived and connected to decoherence between singly excited states. A correlation vector is connected to decoherence between the ground state and the doubly excited state. Optical decoherence between the ground and singly excited states involves linear combinations of the correlation and tuning vectors. Excitonic coupling modifies the tuning vector. The correlation and tuning vectors are not always orthogonal, and both can be asymmetric under pigment exchange, which affects energy transfer. For equal pigment vibrational frequencies, the nonadiabatic tuning vector becomes an anti-correlated delocalized linear combination of intramolecular vibrations of the two pigments, and the nonadiabatic energy transfer dynamics become separable. With exchange symmetry, the correlation and tuning vectors become delocalized intramolecular vibrations that are symmetric and antisymmetric under pigment exchange. Diabatic criteria for vibrational-excitonic resonance demonstrate that anti-correlated vibrations increase the range and speed of vibronically resonant energy transfer (the Golden Rule rate is a factor of 2 faster). A partial trace analysis shows that vibronic decoherence for a vibrational-excitonic resonance between two excitons is slower than their purely excitonic decoherence.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1039/d2ja00114d
