Reflectance Hyperspectral Imaging for Investigation of Works of Art

Sep 28, 2016 - Diffuse reflectance hyperspectral imaging, or reflectance imaging spectroscopy, is a sophisticated technique that enables the capture o...
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Reflectance Hyperspectral Imaging for Investigation of Works of Art: Old Master Paintings and Illuminated Manuscripts Costanza Cucci,*,† John K. Delaney,‡,§ and Marcello Picollo† †

Istituto di Fisica Applicata “Nello Carrara” - National Research Council (IFAC-CNR), Via Madonna del Piano, 10, 50019 Sesto Fiorentino (Florence), Italy ‡ Scientific Research Department, National Gallery of Art, Fourth and Constitution Ave NW, Washington D.C. 20565, United States § Department of Bioengineering, School of Engineering and Applied Science, The George Washington University, Washington, D.C. 20052, United States CONSPECTUS: Diffuse reflectance hyperspectral imaging, or reflectance imaging spectroscopy, is a sophisticated technique that enables the capture of hundreds of images in contiguous narrow spectral bands (bandwidth < 10 nm), typically in the visible (Vis, 400−750 nm) and the near-infrared (NIR, 750− 2500 nm) regions. This sequence of images provides a data set that is called an image-cube or file-cube. Two dimensions of the image-cube are the spatial dimensions of the scene, and the third dimension is the wavelength. In this way, each spatial pixel in the image has an associated reflectance spectrum. This “big data” image-cube allows for the mining of artists’ materials and mapping their distribution across the surface of a work of art. Reflectance hyperspectral imaging, introduced in the 1980s by Goetz and coworkers, led to a revolution in the field of remote sensing of the earth and near planets (Goetz, F. H.; Vane, G.; Solomon, B. N.; Rock, N. Imaging Spectrometry for Earth Remote Sensing. Science, 1985, 228, 1147−1152). In the subsequent decades, thanks to rapid advances in solid-state sensor technology, reflectance hyperspectral imaging, once only available to large government laboratories, was extended to new fields of application, such as monitoring agri-foods, pharmaceutical products, the environment, and cultural heritage. In the 2000s, the potential of this noninvasive technology for the study of artworks became evident and, consequently, the methodology is becoming more widely used in the art conservation science field. Typically hyperspectral reflectance image-cubes contain millions of spectra. Many of these spectra are similar, making the reduction of the data set size an important first step. Thus, image-processing tools based on multivariate techniques, such as principal component analysis (PCA), automated classification methods, or expert knowledge systems, that search for known spectral features are often applied. These algorithms seek to reduce the large number of high-quality spectra to a common subset, which allow identifying and mapping artists’ materials and alteration products. Hence, reflectance hyperspectral imaging is finding its place as the starting point to find sites on polychrome surfaces for spot analytical techniques, such as X-ray fluorescence, Raman spectroscopy, and Fourier transform infrared spectroscopy. Reflectance hyperspectral imaging can also provide image products that are a mainstay in the art conservation field, such as color-accurate images, broadband near-infrared images, and false-color products. This Account reports on the research activity carried out by two research groups, one at the “Nello Carrara” Institute of Applied Physics of the Italian National Research Council (IFAC−CNR) in Florence and the other at the National Gallery of Art (NGA) in Washington, D.C. Both groups have conducted parallel research, with frequent interchanges, to develop multispectral and hyperspectral imaging systems to study works of art. In the past decade, they have designed and experimented with some of the earliest spectral imaging prototypes for museum applications. In this Account, a brief presentation of the hyperspectral sensor systems is given with case studies showing how reflectance hyperspectral imaging is answering key questions in cultural heritage.

1. INTRODUCTION

quality controls, agri-food, pharmaceuticals, and, as presented here, to cultural heritage science.2−6 Hyperspectral imaging consists of a collection of images in a sufficient number of contiguous spectral bands to allow generation of a reflectance spectrum at each pixel in the

Reflectance hyperspectral imaging was the result of intense research activity started in the 1980s and led by Goetz within several NASA Projects.1 The goal of this research was to develop new technologies for remotely observing the earth, and the obtained result revolutionized the field of remote sensing. Subsequently, hyperspectral imaging became well-established and progressively spread out to other fields, such as industrial © 2016 American Chemical Society

Received: January 29, 2016 Published: September 28, 2016 2070

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Figure 1. Scheme of the reflectance hyperspectral image-cube and spectra from two pixels, the floor and the blue surrounding Christ. Pacino di Bonaguida’s Christ in Majesty with Twelve Apostles, Rosenwald Collection, NGA.

instrumentation and the application of reflectance hyperspectral imaging to the study of artworks are presented through examples involving paintings and illuminated manuscripts.

image. Typically, the acquired data set includes hundreds of images corresponding to very narrow bands (90% (Figure 6). The extended NIR camera (1000−2450 nm) 2075

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Figure 7. (a) Color image of the Virgin Mary panel by Cosimo Tura (31 × 12.4 cm2, 1475), Samuel H. Kress Collection, NGA. (b) False-color map created with the SAM algorithm, which identifies pixels in the hyperspectral cube whose reflectance spectra match the endmember spectra shown in (c). Pixels are assigned to an endmember if the angle is less than or equal to the tolerance angle.22

reflectance spectra collected, multivariate statistics were used to reduce the dimensionality of the data set. In this example, PCA reduced the dimensionality from 536 to 13. The PPI algorithm was used to find ∼10 000 spectrally pure pixels for clustering in the 13-dimensional PC space. The reflectance spectra identified by the PPI algorithm were manually clustered in PC space, resulting in eight spectral endmembers that best represented the painting. Mapping the location of endmembers was performed using the SAM algorithm. This algorithm returns a metric for the degree of match between the spectrum at each pixel and the reference endmember spectra. The resulting hyperspectral maps show the spatial distribution of these endmembers. The eight-reflectance spectral endmembers (Figure 7c) were found to well describe the majority of the painting as seen in the map given in Figure 7b. Identification of the pigments present in the endmember spectra was done by comparing their spectral features with those of reference spectra from artist pigments. The endmember spectra that could be uniquely assigned include the following: gypsum for the ground layer, azurite for the blue sky, natural ultramarine for Mary’s robe, iron oxides for the hair and face, and an insect-based organic red lake for Mary’s tunic. The use of lead white and the pigment used for the landscape could not be conclusively identified with hyperspectral reflectance imaging in this case. The identification of lead white relies on the presence of a hydroxyl feature at 1447 nm, which is from hydrocerussite, 2Pb(CO3)·(OH)2, the basic form of cerussite (PbCO3), a lead carbonate. Before the 17th century, lead white could be a mixture of cerussite and hydrocerussite, making the 1447 nm feature less noticeable. Gypsum has three hydroxyl features noted above and the first is at 1447 nm. Assignment of lead

uses a digital ROIC InSb detector array [1024 (spatial) by 1280 (spectral) pixels, 12 μm pixel size, 2.8 nm sampling] operating at 70 K. A telecentric relay optic with an external pupil is used to match the cold stop of the F/D 2.3 InSb camera. The cold stop of the IR camera dewar and the use of two band-pass cold filters (1000−2450 nm light) limits midwave infrared radiation from reaching the detector and thus improving signal-to-noise at long exposures (100−200 ms per line). These compact portable systems can be operated in two modes, either by pushbroom scanning (by moving the painting in front of the hyperspectral cameras by using a computer-controlled 2-D easel capable of scanning an area of 1.5 m2) or whiskbroom scanning (using an internal scan mirror).19 The data acquired by both cameras are calibrated to apparent reflectance and reconstructed in a continuous image cube from 400 to 2500 nm with 750 images, 2.5−2.8 nm spectral sampling, and 160 μm spatial sampling. Using these cameras, together with an InGaAs version (967−1680 nm, 3.4 nm sampling), paintings as large as 1.7 × 1.8 m have been imaged and analyzed. NGA hyperspectral camera systems and mapping algorithms have been used to determine the spatial distribution of artists’ materials or “maps” based on their spectral features. Maps of artists’ materials from works by early Italian artists to modern painters have been completed.11,19,22−24,27 The results obtained from examining a 15th century panel painting by Cosimo Tura are reviewed here. The painting depicts the Virgin Mary (Figure 7a), and is one of four panels that comprise The Annunciation with Saint Francis and St. Louis of Toulouse, ca.1475.22 Image cubes (400−1680 nm) of the panel were collected using two hyperspectral cameras and registered together.26 Because of the large number of 2076

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Accounts of Chemical Research white in the presence of gypsum is possible if the depth of the first band at 1447 nm shows a larger depth than what is expected for gypsum alone. The pigment used for the landscape was tentatively identified as umber (iron and manganese oxides, a natural brown earth pigment) by its spectral shape. However, the collection of XRF element maps showed copper is present in the landscape, an element not found in umber. The best match to both the XRF and reflectance results was a degraded green copper resinate, Cu(C19H29COO)2, which is known to turn brown over time.22 Thus, what appears as a rocky landscape was once green. The use of complementary methods, such as hyperspectral reflectance imaging and XRF point analysis or scanning measurements, has been important for definitive pigment assignments, especially for those pigments that have simple reflectance spectra, such as a single transition edge seen in some yellow pigments. By extending further into the NIR, specifically from 1700 to 2450 nm, the identification and mapping of some paint binders has been possible. Two papers have shown the weak absorption features associated with oils, proteins, and carbohydrates can be used to identify drying oils, egg yolk tempera, wax, proteinbased glues, and gums.23,24 For example, the specific use of egg yolk and animal skin glue was mapped using NIR hyperspectral imaging in a Cosimo Tura painting consisting of four panels, one of which is the panel depicting the Virgin Mary as discussed above. The maps showed the artist selectively used animal skin glue for blue pigments and egg yolk for an insectbased red lake pigment.24 These findings match prior results obtained by HPLC analysis of microsamples taken from the panels.24 A more challenging and unexpected case was the mapping of paint binders in a 15th century illuminated parchment foil by Lorenzo Monaco, where microsampling was not possible. While gum Arabic or protein would have been the expected paint binders’, evidence for the use of egg yolk tempera, used for panel painting during this time period, was found by its absorption feature at 2309 nm. This vibrational feature is associated with the methylene functional groups found in egg yolk lipids.23,24 The egg yolk tempera binder was only found in the painted figure of the prophet (Figure 8).23

Figure 8. NIR image cube analysis of Lorenzo Monaco’s Praying Prophet, Rosenwald Collection (1410−1413). (a) Color image. (b) Projection map of 10-D clustering: the green, blue, and red points define the clusters used for the three endmember spectra in (c). The false-color image (d) shows the pixels whose spectra do not deviate more than the tolerance angle.23 The red areas show evidence for the presence of egg yolk, and the green and blue do not. The green areas show vibrational features assignable to azurite that was mixed with an unknown yellow to create the green initial.

features useful for the identification of artist’ materials. However, preliminary studies show the concerns of heating, challenges in spectral interpretation, and high camera costs, and suggest that progress will be slower than in the visible and nearinfrared.46 Nevertheless, reflectance hyperspectral imaging is finding its place as the starting point for diagnostic study, specifically because it can access the entire surface of a painting.

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5. CONCLUSIONS Since the 2000s, standoff reflectance hyperspectral imaging has emerged as a promising technique for the examination of paintings and works on paper. In the last two decades the complementary research carried out at IFAC−CNR and NGA laboratories, as well as others, has shown the utility of hyperspectral imaging to not only inspect and document artworks, but to provide improved visualization of underdrawings and compositional paint changes, as well as to identify and map artists’ materials in situ. Works of art are inherently challenging to study because of the diversity of materials used, the stratigraphy of the paint layers present, and the range of object sizes encountered. While progress has been made in developing hyperspectral cameras to capture image-cubes of art objects, including large frescoes and mural paintings at archeological sites, challenges remain.43,44 Among these are the need for better algorithms specific for automatic mapping and separation of pigment mixtures.27,45 To support this, reflectance spectral databases tailored to cultural heritage are required. The development of detector arrays and dispersive optical systems offer extending the spectral range in the UV and mid-IR, regions known to be rich in spectral

AUTHOR INFORMATION

Corresponding Author

*E-mail: [email protected]. Notes

The authors declare no competing financial interest. Biographies Costanza Cucci got Physics “Laurea” and Ph.D. in Conservation Science. She is a researcher at IFAC−CNR. Her research focus is on spectroscopic techniques applied to materials characterization in the fields of cultural heritage, environmental monitoring, and safety food controls. John K. Delaney, Ph.D., is the Senior Imaging Scientist at the NGA, where his research focuses on the application of remote sensing imaging methods for the study of works of art. Marcello Picollo, Ph.D., is a researcher at IFAC−CNR. His interests include color measurement, Vis-NIR hyperspectral imaging, and spot size UV−Vis-IR spectroscopic investigations of 2D polychrome objects. 2077

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for Arts and Archaeology. Proc. SPIE 5857; Pezzati, L., Salimbeni, R., Eds.; SPIE: Munich, 2005; pp 58570M−58570M-8. (16) Picollo, M.; Bacci, M.; Casini, A.; Lotti, F.; Poggesi, M.; Stefani, L. Hyperspectral image spectroscopy: a 2D approach to the investigation of polychrome surfaces. In Proc. Conservation Science, Milan, May 10−11, 2007; Townsend, J., Toniolo, L., Capitelli, F., Eds.; Archetype Publications: London, 2008; pp 162−168. (17) Casini, A.; Lotti, F.; Picollo, M.; Stefani, L.; Buzzegoli, E. Image Spectroscopy mapping technique for non-invasive analysis of paintings. Stud. Conserv. 1999, 44, 39−48. (18) Bacci, M.; Casini, A.; Cucci, C.; Muzzi, A.; Porcinai, S. A study on a set of drawings by Parmigianino: integration of art-historical analysis with imaging spectroscopy. J. Cult. Herit. 2005, 6, 329−336. (19) Delaney, J. K.; Zeibel, J. G.; Thoury, M.; Littleton, R.; Palmer, M.; Morales, K. M.; Rie, E. R. D. L.; Hoenigswald, A. Visible and Infrared Imaging Spectroscopy of Picasso’s Harlequin Musician: Mapping and Identification of Artist Materials in situ. Appl. Spectrosc. 2010, 64, 584−594. (20) Baronti, S.; Casini, A.; Lotti, F.; Porcinai, S. Multispectral imaging system for the mapping of pigments in works of art by the use of principal-component analysis. Appl. Opt. 1998, 37, 1299−1309. (21) Mansfield, J. R.; Sowa, M. G.; Majzels, C.; Collins, C.; Cloutis, E.; Mantsch, H. H. Near infrared spectroscopic reflectance imaging: supervised vs. unsupervised analysis using an art conservation application. Vib. Spectrosc. 1999, 19, 33−45. (22) Dooley, K. A.; Conover, D. M.; Glinsman, L. D.; Delaney, J. K. Complementary Standoff Chemical Imaging to Map and Identify Artist Materials in an Early Italian Renaissance Panel Painting. Angew. Chem. 2014, 126, 13995−13999. (23) Ricciardi, P.; Delaney, J. K.; Facini, M.; Zeibel, J. G.; Picollo, M.; Lomax, S. Q.; Loew, M. H. Near Infrared Reflectance Imaging Spectroscopy to Map Paint Binders In Situ on Illuminated Manuscripts. Angew. Chem., Int. Ed. 2012, 51, 5607−5610. (24) Dooley, K. A.; Lomax, S.; Zeibel, J. G.; Miliani, C.; Ricciardi, P.; Hoenigswald, A.; Loew, L.; Delaney, J. K. Mapping of egg yolk and animal skin glue paint binders in Early Renaissance paintings using near infrared reflectance imaging spectroscopy. Analyst 2013, 138, 4838−4848. (25) Kim, S. J.; Zhuo, S.; Deng, F.; Fu, C. W.; Brown, M. S. Interactive visualization of hyperspectral images of historical documents. IEEE Transactions on Visualization and Computer Graphics. 2010, 16, 1441−1448. (26) Conover, D. M.; Delaney, J. K.; Loew, M. H. Automatic registration and mosaicking of technical images of Old Master paintings. Appl. Phys. A: Mater. Sci. Process. 2015, 119, 1567−1575. (27) Conover, D. Fusion of Reflectance and X-ray Fluorescence Imaging Spectroscopy Data For The Identification of Artists’ Materials. Ph.D. Thesis, George Washington University, Washington, DC, 8/31/15. (28) Clark, R. N.; King, T. V. V; Klejwa, M.; Swayze, G.; Vergo, N. J. High spectral resolution reflectance spectroscopy of minerals. J. Geophys. Res. 1990, 95, 12653−12680. (29) Fabian, J.; Hartmann, H. Light Absorption of Organic Colorants: Theoretical Treatment and Empirical Rules; Springer-Verlag, New York, 1980. (30) Jackall, Y.; Delaney, J. K.; Swicklik, M. Portrait of a Woman with a Book: A “newly discovered” fantasy figure by Jean- Honoré Fragonard at the National Gallery of Art, Washington. Burlington Magazine 2015, 157, 1345. (31) Daffara, C.; Pampaloni, E.; Pezzati, L.; Barucci, M.; Fontana, R. Acc. Chem. Res. 2010, 43, 847−856. (32) Thomson, G. The museum environment, Second ed.: Butterworth- Einhemann Elsevier: London-Boston-Singapore-Sydney-Toronto-Wellington, 2013; Chapter 1. (33) Frey, F.; Heller, D.; Kushel, D.; Vitale, T.; Weaver, G. AIC Guide to Digital Photography and Conservation Documentation; Warda, J., Ed.; American Institute for Conservation of Historic and Artistic Works: Washington, D.C., 2008.

ACKNOWLEDGMENTS J.K.D. thanks K. Dooley for help and acknowledges support from the Andrew W. Mellon and Samuel H. Kress Foundations and the National Science Foundation (1041827). C.C. and M.P. are extremely grateful to their colleagues A. Casini and L. Stefani for the development of the IFAC−CNR scanner and to the colleagues of the former Soprintendenza SPSAE e per il Polo Museale della città di Firenze and the Soprintendenza BAPSAE di Firenze Pistoia e Prato of the Italian MIBACT for having allowed the access and measurements on the artworks.

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REFERENCES

(1) Goetz, F. H.; Vane, G.; Solomon, B. N.; Rock, N. Imaging Spectrometry for Earth Remote Sensing. Science 1985, 228, 1147− 1152. (2) Gowen, A. A.; O’Donnell, C. P.; Cullen, P. J.; Downey, G.; Frias, J. M. Hyperspectral imaging − an emerging process analytical tool for food quality and safety control. Trends Food Sci. Technol. 2007, 18, 590−598. (3) Tatzer, P.; Wolf, M.; Panner, T. Industrial application for inline material sorting using hyperspectral imaging in the NIR range. RealTime Imaging. 2005, 11, 99−107. (4) Hamilton, S. J.; Lodder, R. A. Hyperspectral imaging technology for pharmaceutical analysis. In Biomedical Nanotechnology Architectures and Applications. Proc. SPIE 4626; Bornhop, D. J., Dunn, D. A., Mariella, R. P., Murphy, C. J., Nicolau, D. V., Nie, S., Palmer, M., Raghavachari, R., Eds.; SPI: San Jose, CA, 2002; pp 136−147. (5) Kellicut, D. C.; Weiswasser, J. M.; Arora, S.; Freeman, J. E.; Lew, R. A.; Shuman, C.; Mansfield, J. R.; Sidawy, A. N. Emerging technology: hyperspectral imaging. Perspect. Vasc. Surg. Endovasc. Ther. 2004, 16, 53−57. (6) Fischer, C.; Kakoulli, I. Multispectral and hyperspectral imaging technologies in conservation: current research and potential applications. Rev. Conserv. 2006, 7, 3−16. (7) Bacci, M.; Cappellini, V.; Carla’, R. Diffuse reflectance spectroscopy: an application to the analysis of art works. J. Photochem. Photobiol., B 1987, 1, 132−133. (8) Bacci, M.; Baldini, F.; Carlà, R.; Linari, R. A Color Analysis of the Brancacci Chapel Frescoes. Appl. Spectrosc. 1991, 45, 26−31. (9) Bacci, M.; Picollo, M. Non-destructive detection of Co(II) in paintings and glasses. Stud. Conserv. 1996, 41, 136−144. (10) Leona, M.; Winter, J. Fiber optics reflectance spectroscopy: a unique tool for the investigation of Japanese paintings. Stud. Conserv. 2001, 46, 153−162. (11) Delaney, J. K.; Ricciardi, P.; Glinsman, L. D.; Facini, M.; Thoury, M.; Palmer, M.; Rie, E. R. D. L. Use of Imaging Spectroscopy, Fiber Optic Reflectance Spectroscopy and X-ray Fluorescence to Map and Identify Pigments in Illuminated Manuscripts. Stud. Conserv. 2014, 59, 91−101. (12) Kubik, M. Hyperspectral imaging: a new technique for the noninvasive study of artworks. In Physical techniques in the study of art, archaeology and cultural heritage; Creagh, D., Bradley, D., Eds.; Elsevier Science: Amsterdam, 2007; Vol. 2, Chapter 5, pp 199−271. (13) Balas, C.; Papadakis, V.; Papadakis, N.; Papadakis, A.; Vazgiouraki, E.; Themelis, G. A novel hyper-spectral imaging apparatus for the non-destructive analysis of objects of artistic and historic value. J. Cult. Herit. 2003, 4, 330−337. (14) Delaney, J. K.; Zeibel, J. G.; Thoury, M.; Litteton, R.; Morales, K. M.; Palmer, M.; René de la Rie, E. Visible and Infrared Reflectance Imaging Spectroscopy of Paintings: Pigment Mapping and Improved Infrared Reflectography. In O3A: Optics for Arts, Architecture, and Archaeology II. Proc. SPIE 7391; Pezzati, L., Salimbeni, R., Eds.; SPIE: Munich, 2009; pp 739103−739103-8. (15) Casini, A.; Bacci, M.; Cucci, C.; Lotti, F.; Porcinai, S.; Picollo, M.; Radicati, B.; Poggesi, M.; Stefani, L. Fiber optic reflectance spectroscopy and hyper-spectral image spectroscopy: two integrated techniques for the study of the Madonna dei Fusi. In Optical Methods 2078

DOI: 10.1021/acs.accounts.6b00048 Acc. Chem. Res. 2016, 49, 2070−2079

Article

Accounts of Chemical Research (34) Bellucci, R.; Frosinini, C. The Madonna of the Yarnwinder: a case study of non-invasive technique for art historians and conservators. In Optical Methods for Arts and Archaeology. Proc. SPIE 5857; Pezzati, L., Salimbeni, R., Eds.; SPIE: Munich, Germany, 2005; pp 58570K−58570K-4. (35) Cucci, C.; Casini, A.; Picollo, M.; Poggesi, M.; Stefani, L. Open issues in hyperspectral imaging for diagnostics on paintings: when high spectral and spatial resolution turns into data redundancy. In O3A: Optics for Arts, Architecture, and Archaeology II. Proc. SPIE 8084; Pezzati, L., Salimbeni, R. Eds.; SPIE: Munich, Germany, 2011; pp 80848.1−80848.10. (36) Cucci, C.; Casini, A.; Picollo, M.; Stefani, L. Extending HyperSpectral Imaging from Vis to NIR spectral regions: a novel scanner for the in-depth analysis of polychrome surfaces. In O3A: Optics for Arts, Architecture, and Archaeology IV. Proc. SPIE 8790; Pezzati, L., Targowski, P., Eds.; SPIE: Munich, Germany, 2013; pp 8790.1− 8790.9. (37) Burger, W.; Burge, M. J. Colorimetric Color Spaces. In Principles of Digital Image Processing. Core Algorithms; Mackie, I., Ed.; Springer: London, 2009; Chapter 6. (38) Berns, R. S. The Science of Digitizing Paintings for ColorAccurate Image Archives: A Review. J. Imaging Sci. Technol. 2001, 45, 305−325. (39) CIE. 15:2004 Technical Report. Colorimetry, 3rd ed.; Commission International de I’Eclairage (CIE), Central Bureau of the CIE: Vienna. (40) Marcus, R. T. The measurement of color. In Color for science, art and technology; Nassau, K., Ed.; Elsevier: Amsterdam, 1998; pp 31−96. (41) Martinez, K.; Cupitt, J.; Saunders, D.; Pillay, R. Ten Years of Art Imaging Research. Proc. IEEE 2002, 90, 28−41. (42) Buzzegoli, E.; Bellucci, R. Michelangelo’s ’Doni Tondo’ investigated with non-invasive analytical techniques. In Studying Old Master Paintings: Technology and Practice. The National Gallery Technical Bulletin 30th Anniversary Conference Postprints; Spring, M., Howard, H., Eds.; Archetype Publications: London, 2011; pp 52−58. (43) Cucci, C.; Picollo, M.; Chiarantini, L.; Sereni, B. Hyperspectral remote sensing techniques applied to the noninvasive investigation of mural paintings: a feasibility study carried out on a wall painting by Beato Angelico in Florence. In Optics for Arts, Architecture, and Archaeology V. Proc. SPIE 9527; Pezzati, L., Targowski, P., Eds.; SPIE: Munich, Germany, 2015; pp 95270P-1−95270P-9. (44) Liang, H. Advances in multispectral and hyperspectral imaging for archaeology and art conservation. Appl. Phys. A: Mater. Sci. Process. 2012, 106 (2), 309−323. (45) Vitorino, T.; Casini, A.; Cucci, C.; Melo, M. J.; Picollo, M.; Stefani, L. Non-invasive identification of traditional red lake pigments in 14th-16th centuries paintings through the use of hyperspectral imaging technique. Appl. Phys. A: Mater. Sci. Process. 2015, 121, 891− 901. (46) Rosi, F.; Miliani, C.; Braun, B.; Harig, R.; Sali, D.; Brunetti, B. G.; Sgamellotti, A. Noninvasive Analysis of Paintings by Mid-infrared Hyperspectral Imaging. Angew. Chem. 2013, 125, 5366−5369.

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