Name: applying hyperspectral reflectance imaging to investigate the palettes and the techniques of painters
Uploaded: 2021-06-18T13:00:00
Description: Watch this Scientific Journal Video about Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters at JoVE.com

This research was funded by Regione Lombardia in the framework of the Project MOBARTECH: una piattaforma mobile tecnologica, interattiva e partecipata per lo studio, la conservazione e la valorizzazione di beni storico-artistici - Call Accordi per la Ricerca e l&#39;Innovazione.
The authors are grateful to the staff at Museo del Novecento for the support during the&#160;in situ&#160;experimental sessions and to the Associazione Pellizza da Volpedo for the access to Studio Museo.

1. Set the spatial resolution of the hypercubes
<ol>
	<li>Perform a preliminary inspection of the painted surface (Figure 1) supported by art experts to identify the main features of the painting.
	<ol>
		<li>Recognize the pictorial techniques employed by the artist to create the painting.</li>
		<li>Identify the different brush strokes of paint on the canvas.</li>
		<li>Estimate, qualitatively, the characteristics of the brush strokes with particular attention to their size.</li>
	</ol>
	...

<ol>
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Hyperspectral reflectance imaging datasets are large reservoirs of information; therefore, the development of robust and, possibly, automated protocols to analyze the data is a key turn to exploit their potential15,17. The proposed algorithm answers this need in the field of cultural heritage with particular attention to the characterization of the pigments of paintings. Based on SAM20,21, the algorithm s...

Reflectance Spectroscopy (RS) and Fiber Optics Reflectance Spectroscopy (FORS) are based on the detection of the light reflected by surfaces once illuminated by a light source, typically a tungsten-halogen lamp. The output of the acquisition system is constituted by spectra where the reflectance is monitored as a function of the wavelength in a range that depends on the characteristics of the employed experimental setup1,2,3. Introduced during the last four decades4,5, RS and FORS are typically used in combination with X-ray fluorescence and other spectroscopies to describe the materials and the techniques used by artists to realize their masterpieces6,7,8,9. The study of the reflectance spectra is usually performed by comparing the data from the sample with a group of reference spectra selected by the user in personal or public databases. Once the reference spectra that comply with the realization period of the sample and with the&#160;modus operandi&#160;of the artist have been identified, the user recognizes the main features of the reflectance spectra (i.e., transition, absorption, and reflection bands1,2,10,11) and then, with the help of other techniques6,7,8 they distinguish&#160;the pigments that have been used in the paintings. Finally they discusses the slight differences that there exist between the references and the experimental spectra7,9.
In most cases, the experimental datasets are composed of a few spectra, collected from areas chosen by art experts and assumed to be significant for the characterization of the painting6, 12,13. Despite the skills and the experience of the user, a few spectra cannot fully exhaust the characteristics of the whole painted surface. Moreover, the result of the analysis will always be strongly dependent on the expertise of the performer. In this scenario, Hyperspectral Reflectance Imaging (HRI3,14,15) could be a useful resource. Instead of a few isolated spectra, the experimental setups return the reflectance properties of extended portions or even of the whole artefact under investigation16. The two main advantages with respect to the acquisition of the isolated spectra are evident. On one hand, the availability of the spatial distribution of the reflectance properties allows the identification of areas that hide interesting features, even though they may not seem peculiar17. On the other hand, the hypercubes guarantee a number of spectra high enough to enable the statistical analysis of the data. These facts support the comprehension of the distribution of pigments within the painted surface18,19.
With HRI, the comparison of the experimental data with the references could be hard to handle15. A typical detector returns hypercubes of at least 256 x 256 spectra. This would require the user to evaluate more than 65,000 reflectance spectra against each reference, a task almost impossible to be carried out manually in a reasonable time. Therefore, the request for robust and, possibly, automated protocols to manage and analyze HRI datasets is more than justified15,17. The proposed method answers this need by handling the whole analytic procedure with the minimum involvement and the maximum flexibility.
An algorithm comprising a set of home-made codes (Table of Materials) reads, manages, and organizes the files returned by the experimental setup. It allows the fine selection of the portions of the Fields of View (FOVs, one field of view is the area of the painting monitored by a single hypercube) to be studied and performs the analysis of the data based on the Spectral Angle Mapper (SAM) method20,21 and on the manipulation of the original spectra. SAM returns false color gray-scale images called similarity maps. The values of the pixels of these maps correspond to the spectral angles that are the angles between the spectra stored in the hypercubes&#160;and the so-called End Members (EMs, a group of reference spectra that should describe the features of the surface monitored by the hypercubes)22. In the case of RS applied to paintings, the EMs are the reflectance spectra of pigments that should match the palette of the Master. They are chosen based on the available information about the artist, the realization period of the painting, and the expertise of the user. Therefore, the output of the SAM is a set of maps that describes the spatial distributions of these pigments over the painting surface and that supports the user to infer the materials used by the artist and their organization in the artefact. The algorithm offers the possibility of employing all kind of references independently from their origin. The references can be specific spectra selected within the hypercubes, come from databases, be acquired by a different instrument on a different surface (such as samples of pigments or the palette of the artist, for instance), or be obtained employing any kind of reflectance spectroscopy, FORS included.
SAM has been preferred among the available classification methods because it has been demonstrated to be effective for characterizing pigments (refer to the book by Richard23&#160;to have an overview of the main available classification methods).&#160;Instead, the idea of developing a home-made protocol rather than adopting one of the many tools freely available on the net24,25 relies on a practical consideration. Despite the effectiveness and scientific foundation of the existing GUIs and software, a single tool hardly satisfies all the needs of the user. There could be an Input/Output (I/O) issue because a tool does not manage the file containing the raw data. There could be an issue regarding the analysis of the data because another tool does not provide the desired approach. There could be a limitation in the handling of the data because the simultaneous analysis of multiple datasets is not supported. In any case, a perfect tool does not exist. Each method must be adjusted to the data or vice versa. Therefore, the development of a home-made protocol has been preferred.
The presented approach offers neither a complete set of analytical methods (see, for comparison, the tool proposed by Mobaraki and Amigo24) nor an easy-to-manage user-interface (see, for comparison, the software employed by Zhu and co-workers25), but, in exchange, it focuses on a still underrated aspect of hyperspectral data analysis: the opportunity to manipulate the detected spectra. The power of the approach is illustrated by applying it to the painting Quarto Stato by Giuseppe Pellizza da Volpedo (Figure 1), an iconic oil on canvas held in the Museo del Novecento in Milan, Italy. Note that, since the approach requires running home-made codes, the developer arbitrarily chose the names of the codes and both the input and output variables used in the description of the protocol. The names of the variables can be changed by the user but they must be provided as follow: the input and out variables must be written respectively within brackets and eventually separated by comma and within square brackets and eventually separated by a white space.&#160;On the contrary the names of the codes cannot be altered.

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>ImageJ/Fiji</td>
			<td>Specim (Oulo, Finlad)</td>
			<td>N/A</td>
			<td>Portable reflectance hyperspectral camera used to acquire the hypercubes</td>
		</tr>
		<tr>
			<td>MATLAB 2019b</td>
			<td>StellarNet Inc (Tampa, Florida, USA)</td>
			<td>N/A</td>
			<td>Portable reflectance spectrometer used to acquire independent reflectance spectra</td>
		</tr>
		<tr>
			<td>Specim IQ Hyperspectral Camera</td>
			<td>National Institutes of Health (Bethesda, Maryland, USA)</td>
			<td>N/A</td>
			<td>&#160;Open source Java image processing program</td>
		</tr>
		<tr>
			<td>StellarNet BLUE-wave Miniature Spectrometer</td>
			<td>&#160;MathWorks (Natick, Massachusset, USA)</td>
			<td>N/A</td>
			<td>Program Language and numerical computing environment</td>
		</tr>
	</tbody>
</table>

applying hyperspectral reflectance imaging to investigate the palettes and the techniques of painters

Reflectance Spectroscopy (RS) and Fiber Optics Reflectance Spectroscopy (FORS) are well-established techniques for the investigation of works of art with particular attention to paintings. Most modern museums put at the disposal of their research groups portable equipment that, together with the intrinsic non-invasiveness of RS and FORS, makes possible the in situ collection of reflectance spectra from the surface of artefacts. The comparison, performed by experts in pigments and painting materials, of the experimental data with databases of reference spectra drives the characterization of the palettes and of the techniques used by the artists. However, this approach requires specific skills and it is time consuming especially if the number of the spectra to be investigated becomes large as is the case of Hyperspectral Reflectance Imaging (HRI) datasets. The HRI experimental setups are multi-dimensional cameras that associate the spectral information, given by the reflectance spectra, with the spatial localization of the spectra over the painted surface. The resulting datasets are 3D-cubes (called hypercubes or data-cubes) where the first two dimensions locate the spectrum over the painting and the third is the spectrum itself (i.e., the reflectance of that point of the painted surface versus the wavelength in the operative range of the detector). The capability of the detector to simultaneously collect a great number of spectra (typically much more than 10,000 for each hypercube) makes the HRI datasets large reservoirs of information and justifies the need for the development of robust and, possibly, automated protocols to analyze the data. After the description of the procedure designed for the data acquisition, we present an analysis method that systematically exploits the potential of the hypercubes. Based on Spectral Angle Mapper (SAM) and on the manipulation of the collected spectra, the algorithm handles and analyzes thousands of spectra while at the same time it supports&#160;the user to unveil the features of the samples under investigation. The power of the approach is illustrated by applying it to Quarto Stato, the iconic masterpiece by Giuseppe Pellizza da Volpedo, held in the Museo del Novecento in Milan (Italy).

The proposed protocol offers a set of interesting features for the management and the analysis of HRI data. The I/O (step 3.1) of the raw data is always the first problem that must be solved before applying any analysis method and it can become a critical issue when dealing with large amounts of data. In the present case, the only task regarding the raw data is to store the experimental results into a dedicated folder and select it by browsing the hard disk when running the reading code (step 3.1.1). Thereafter, the crop...

Watch this Scientific Journal Video about Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters at JoVE.com

Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters

Hyperspectral Reflectance Imaging hypercubes include remarkable information into a large amount of data. Therefore, the request for automated protocols to manage and study the datasets is widely justified. The combination of Spectral Angle Mapper, data manipulation, and a user-adjustable analysis method constitutes a key-turn for exploring the experimental results.

Reflectance Spectroscopy (RS) and Fiber Optics Reflectance Spectroscopy (FORS) are well-established techniques for the investigation of works of art with ...

This protocol offers new opportunities involving the analysis of paintings of faces. It supports the users step by step along the whole data analysis process. This protocol has two main advantages.The users can tailor the analysis according to their preferences, and for the first time, spectrum manipulation has been introduced to analyze the hyperspectral data. Hyperspectral reflectance imaging is successfully used to study skin disease or tumor diagnosis. Even if a protocol was born in the counter heritage field it can be applied to clinical health dataset as well.With the support of art experts, perform a preliminary inspection of the painted surface to identify the main features of the painting. Make a note of the pictorial techniques used by the artist, the different brush strokes of paint on the canvas, and qualitatively estimate the characteristics of the brush strokes with particular attention to their size. Create ad hoc samples where the brush strokes show characteristics similar to those applied by the artist by mimicking the pictorial technique used by the artist.Acquire the hyperspectral data and check whether the spatial resolution of the hypercubes can distinguish the different brushstrokes on the RGB images of the painted surface. Run PointSel, the isolated measuring point selection code to manually select some reference spectra on the surfaces of the test samples. Type the command line including the semicolon in the terminal window and press enter to run the code.Select the measurement points by clicking the interactive window that one by one shows the two dimensional RGB images of the fields of view. Run SAM_Standard, the standard SAM maps evaluation code to extract the SAM maps using the whole spectra. Type the command line, including semicolon in the terminal window, then press enter to run the code.SAM maps are saved as PNG images in the current work folder. Check whether the obtain similarity maps display the details of the brush strokes used to realize the test samples. If not, restart the process by readjusting the distance between the test sample surface and the acquisition equipment.According to the evaluation obtained by the test samples set the distance between the surface of the painting under investigation and the acquisition equipment. Perform IO of the hyperspectral data by organizing, reading and managing the hypercubes. Run the HS FileLister code to store the list of the files containing the hypercubes and the related information into two variables at the disposal of the algorithm.Run the HS_Crop PNG code to select the portion of each FOV to be used in the analysis of the data. Next, run the PointSel code and click within the displayed interactive window to identify the reference spectra as isolated measuring points over the surface of the monitored areas. Type the command line including the semicolon in the terminal window and press enter to run ReticularSel, the Reticular selection code.This automatically selects the reference spectra as a regular reticulum of measuring points superimposed to the surface of the monitored areas. This selection method makes the analysis very time consuming as the number of references is large. Type the command line including the semicolon in the terminal window and press enter to run SaveImPoint.This saves a location at the selected measurement point superimposed to the pictures of the fields of view. Run Spectra_Importer, the external references importer code to create a variable containing references from data sets and databases independent from the hypercubes acquired on quarto stato. Note that the spectra have different size with respect to those obtained with the hyperspectral camera.Run the SAM complete code to evaluate the similarity maps. Feed the code with the desired pre-processing option entering zero or one in the dialogue box. Zero to require the spectra normalization only or one to require that after the normalization the spectra are derived one time.enter this sequence of numbers corresponding to the desired columns of the references matrix into the dialogue box by typing the numbers separated by a white space. Press Enter to continue. Set the method to zero for no manipulation of the data.One to require manual selection of the wavelength ranges of the spectra to be considered before starting the analysis or two to require the algorithm to order the data based on a specific criterion and before the evaluation of the SAM maps. To select the end members for the SAM analysis, the algorithm either retrieves the references spectra among the hypercubes by manually selecting some isolated measuring points or automatically samples the painting surface, providing a particular selection of measuring points within one or more FOVs. The algorithm can also compare the hypercubes with external spectra like the ones obtained by a portable FORS miniature spectrometer.When the pre-processed references appear on an interactive window, one or more wavelength intervals to be analyzed can be manually selected. In automatic selection, the algorithm computes a maximum variance within the desired references and orders spectra according to this criterion. If the maximum variance corresponds to the nth wavelength the content of the nth component of each pre-processed spectrum will be moved to the first position of a rearranged hypervector and so on.Following the automatic manipulation, the algorithm applies a floating threshold to the variance values and evaluates the SAM maps at the increasing threshold which results in a total of two n plus one sets of maps where n is a number of values assumed by the threshold. The obtained similarity maps provide new insights into the details of the mapped area. They can help to compare the samples and the references.The possibility to customize the analysis and to exploit any spectrum as reference, expands the user horizons but simultaneously asks the user for a careful evaluation of their choices This approach allows the use of spectrum manipulation as an analysis tool, therefore computer vision and statistical studies can help deepen the knowledge about the possibility of this matter.

Research

JoVE Journal

Chemistry

Origami Inspired Self-assembly of Patterned and Reconfigurable Particles

There are numerous techniques such as photolithography, electron-beam lithography and soft-lithography that can be used to precisely pattern two ...

Isolation and Chemical Characterization of Lipid A from Gram-negative Bacteria

Lipopolysaccharide (LPS) is the major cell surface molecule of gram-negative bacteria, deposited on the outer leaflet of the outer membrane bilayer. LPS ...

Formation of Ordered Biomolecular Structures by the Self-assembly of Short Peptides

In nature, complex functional structures are formed by the self-assembly of biomolecules under mild conditions. Understanding the forces that control ...

Synthesis of Protein Bioconjugates via Cysteine-maleimide Chemistry

Synthesis of Protein Bioconjugates via Cysteine-maleimide Chemistry

The chemical linking or bioconjugation of proteins to fluorescent dyes, drugs, polymers and other proteins has a broad range of applications, such as the ...

Using Capillary Electrophoresis to Quantify Organic Acids from Plant Tissue: A Test Case Examining Coffea arabica Seeds

Using Capillary Electrophoresis to Quantify Organic Acids from Plant Tissue: A Test Case Examining Coffea arabica Seeds

Carboxylic acids are organic acids containing one or more terminal carboxyl (COOH) functional groups. Short chain carboxylic acids (SCCAs; carboxylic ...

Regioselective O-Glycosylation of Nucleosides via the Temporary 2',3'-Diol Protection by a Boronic Ester for the Synthesis of Disaccharide Nucleosides

Regioselective O-Glycosylation of Nucleosides via the Temporary 2',3'-Diol Protection by a Boronic Ester for the Synthesis of Disaccharide Nucleosides

Disaccharide nucleosides, which consist of disaccharide and nucleobase moieties, have been known as a valuable group of natural products having ...

Ion Mobility-Mass Spectrometry Techniques for Determining the Structure and Mechanisms of Metal Ion Recognition and Redox Activity of Metal Binding Oligopeptides

Electrospray ionization (ESI) can transfer an aqueous-phase peptide or peptide complex to the gas-phase while conserving its mass, overall charge, ...

Solid Phase 11C-Methylation, Purification and Formulation for the Production of PET Tracers

Solid Phase 11C-Methylation, Purification and Formulation for the Production of PET Tracers

Routine production of radiotracers used in positron emission tomography (PET) mostly relies on wet chemistry where the radioactive synthon reacts with a ...

Hyperspectral Imaging as a Tool to Study Optical Anisotropy in Lanthanide-Based Molecular Single Crystals

In this work, we describe a protocol for a novel application of hyperspectral imaging (HSI) in the analysis of luminescent lanthanide (Ln3+)-based ...

Direct Comparison of Hyperspectral Stimulated Raman Scattering and Coherent Anti-Stokes Raman Scattering Microscopy for Chemical Imaging

Stimulated Raman scattering (SRS) and coherent anti-Stokes Raman scattering (CARS) microscopy are the most widely used coherent Raman scattering imaging ...