Molecular Fluorescence Imaging Spectroscopy for Mapping Low Concentrations of Red Lake Pigments: Van Gogh's Painting The Olive Orchard

2020 ◽  
Vol 132 (15) ◽  
pp. 6102-6109
Author(s):  
Kathryn A. Dooley ◽  
Annalisa Chieli ◽  
Aldo Romani ◽  
Stijn Legrand ◽  
Costanza Miliani ◽  
...  
2020 ◽  
Vol 59 (15) ◽  
pp. 6046-6053
Author(s):  
Kathryn A. Dooley ◽  
Annalisa Chieli ◽  
Aldo Romani ◽  
Stijn Legrand ◽  
Costanza Miliani ◽  
...  

2020 ◽  
Vol 8 (1) ◽  
Author(s):  
John K. Delaney ◽  
Kathryn A. Dooley ◽  
Annelies van Loon ◽  
Abbie Vandivere

AbstractAs part of the 2018 Girl in the Spotlight project, reflectance and molecular fluorescence imaging spectroscopy (RIS, FIS), and macroscale X-ray fluorescence scanning (MA-XRF) were performed on Johannes Vermeer’s Girl with a Pearl Earring (c. 1665, Mauritshuis) with the goal of obtaining a comprehensive understanding of the distribution of pigments. Prior analysis of cross-sections in the 1990s, and their recent (re)-examination identified many of the pigments present in the painting. The results from all three imaging methods, along with site-specific fibre optic reflectance spectroscopy, confirmed Vermeer’s limited palette and determined how and where each pigment was used. RIS and MA-XRF found the blue region of the headscarf was painted with natural ultramarine and varying amounts of lead white. The yellow part of the headscarf was painted with yellow ochre and lead white, and the border of the headscarf additionally contained ultramarine and lead–tin yellow. The lit side of the jacket was painted with yellow ochre, lead white, and ultramarine, whereas the darker, ruddy blue-green areas that are in relative shadow contained yellow ochre with ultramarine. FIS also mapped a red lake in portions of the shadowed areas at the back of the jacket. The Girl’s skin was painted using earths (ochres), lead white, vermilion, and some red lake. Fluorescence emission from red lake was strongest in the lips, where vermilion was also found. The pearl earring was depicted using a scumble and highlight of lead white. In the dark background, the RIS data cube allowed the determination of the visible spectral shape even though the overall reflectance intensity was low (1 to 3%). A reflectance inflection point at ~ 700 nm indicated the presence of indigo, whereas lack of a reflectance peak at green wavelengths in most areas indicated degradation of the yellow pigment previously identified as weld. Some small green areas in the background (i.e. reflectance maximum at 525 nm) were found; these coincided with areas previously protected by old retouchings, and are thus better preserved. The combination of all three spectral imaging modalities provided a more complete understanding of how the colouration of the painting was achieved.


ACS Nano ◽  
2013 ◽  
Vol 7 (8) ◽  
pp. 7420-7427 ◽  
Author(s):  
Bin Kang ◽  
Marwa M. Afifi ◽  
Lauren A. Austin ◽  
Mostafa A. El-Sayed

2013 ◽  
Vol 2013 ◽  
pp. 1-6 ◽  
Author(s):  
Caio B. Wetterich ◽  
Ratnesh Kumar ◽  
Sindhuja Sankaran ◽  
José Belasque Junior ◽  
Reza Ehsani ◽  
...  

The overall objective of this work was to develop and evaluate computer vision and machine learning technique for classification of Huanglongbing-(HLB)-infected and healthy leaves using fluorescence imaging spectroscopy. The fluorescence images were segmented using normalized graph cut, and texture features were extracted from the segmented images using cooccurrence matrix. The extracted features were used as an input into the classifier, support vector machine (SVM). The classification results were evaluated based on classification accuracies and number of false positives and false negatives. The results indicated that the SVM could classify HLB-infected leaf fluorescence intensities with up to 90% classification accuracy. Though the fluorescence intensities from leaves collected in Brazil and the USA were different, the method shows potential for detecting HLB.


2011 ◽  
Vol 140 (5) ◽  
pp. S-757
Author(s):  
Bhaskar Banerjee ◽  
Timothy Renkowski ◽  
Piyush Tiwari ◽  
Vassiliki L. Tsikitis ◽  
Urs Utzinger

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