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.

<ol>
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The authors have nothing to disclose.

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 supports the user during the whole analysis process from the setting of the experimental conditions to the evaluation of the distribution of pigments. Though the algorithm still does not have a complete graphical interface and that it does not provide a tool for viewing the results (for this purpose an open-source software has been used31 and it is recommended, see Table of Materials), the set of possibilities implemented to modulate the approach to the data analysis extensively balances these drawbacks.
The protocol sets the acquisition system according to the characteristics of both the sample and the detector. On one hand, the Divisionist technique employed by Pellizza Da Volpedo to create Quarto Stato requires that the hypercubes distinguish between small brush strokes of different pigments placed side by side. On the other hand, the hyperspectral camera has a focus range between 150 mm and infinite with a manual adjustment system that at 1 m distance to the target&#160;detects an area of 0.55 x 0.55 m with a spatial resolution of 1.07 mm26. The application of the algorithm to few hypercubes acquired on the test samples (Figure 2) helps establish a suitable working distance for the data acquisition. The observation of the measurements allows to set the working distance for the experimental campaign to 30 cm, corresponding to a resolution of 0.31 mm at the target. &#160;This working distance was also successfully adopted during the experimental sessions conducted on&#160;Quarto Stato&#160;(Figure 3). Once the working distance was defined, the illumination of the surface of the sample remains a critical issue3,15. When a portion of a FOV shows uneven (Red Circle in Figure 4A) instead of uniform illumination (Blue Circle in Figure 4B), the reflectance properties change dramatically (Figure 4C) and the whole procedure is compromised (Figure 4D vs. Figure4E). The protocol prevents uneven illumination (and more in general against artefacts in the monitored areas) during the acquisition of the data (by returning RGB, step 1.4.1, and SAM maps, step 1.4.9, that can be checked by the user) and a posteriori by excluding the compromised portions of the FOVs from the analysis by means of the cropping code (steps 1.4.2 and 3.1.2).
The protocol allows the user to select the references (i.e., the end members used for the evaluation of the SAM maps) with the maximum freedom. On one hand, the EMs can be chosen within the edges of the hypercubes by two manners: isolated measuring points selection (Figure 5A in steps 1.4.5 and 3.1.2) or reticular measuring points selection (Figure 5B in step 3.1.3). The first can be defined as informed selection because it requires some expertise in the user to manually identify the significant measuring points. The latter can be defined as blind selection because the reticular sampling of the FOVs requires only the value of the sampling interval to be performed. On the other hand, the EMs can be retrieved from outside the painting under investigation (step 3.1.4). During the experimental campaign conducted on Quarto Stato, a portable miniature FORS spectrometer (Table of Materials) was used to collect spectra from draft samples belonging to the artist and currently kept in the Studio Museum located in Volpedo (Pellizza da Volpedo Studio Museo, Volpedo (AL), Italy). These reflectance data have been used for the evaluation of the SAM maps and some of them are reported in Figure 6 and Figure 7. Since it limits the importance of the absolute intensity and of the baseline of the spectra, the pre-processing is mandatory for both the hypercubes and the EMs, especially if they have been obtained from slightly different setups or operative conditions32.
The last main feature of the protocol is the chance to manipulate the experimental data. For manipulation, it is intended that the identification of the most significant components of the EMs (i.e., of those portions of the spectra of the end members should help characterize the materials used by the artist). This task can be accomplished manually (Figure 6) or automatically (Figure 7). In the first case, the algorithm takes advantage of the expertise of the performer while, in the second case, it is a statistical criterion that determines the components that, time by time, will be used to evaluate the SAM maps. In both cases, the manipulation increases the number of the resulting similarity maps and consequently extends the capability to disclose the information carried by the hypercubes. In particular, the criterion-based selection generates a great number of insights of the painted surface (Figure 8).
Taken individually, the enumerated features could appear as mere technical benefits, but together they imply at least two main key points. The algorithm can be successfully applied by any kind of user and it can significantly broaden the scenario of the analysis. In fact, the main steps of the protocol (i.e., the selection of the references and the manipulation of the data) can be performed automatically, disregarding the skills and the experience of the user. With the possibility to drive the analysis with spectra from outside the hypercubes, all the reflectance data in the disposal of the researchers can be exploited for the characterization of the sample under investigation.
In summary, the protocol can be an extremely flexible tool. With some improvements regarding the graphical interface and the number of supported analysis methods, it can be a step beyond the state of the art regarding the handling and the analysis of data obtained from painted surfaces by means of hyperspectral reflectance imaging.

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><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>&amp;nbsp;Open source Java image processing program</td></tr><tr><td>StellarNet BLUE-wave Miniature Spectrometer</td><td>&amp;nbsp;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 cropping and the RGB rebuilding codes allow the refinement of the selection of the data to be analyzed (step 3.1.2) and checks that the experimental conditions have been properly set at the moment of the acquisition of the hypercubes (step 3.1.3, see Figure 4 and the Discussion section for further details).
Once verified that the data-cubes have been correctly acquired, the algorithm offers different possibilities to select the end members for the SAM analysis20,21 (step 3.2). The first two options (steps 3.2.1 and 3.2.2) retrieve the references among the hypercubes by manually selecting some isolated measuring points (Figure 5A) or by automatically sampling the surface of the painting providing a reticular selection of measuring points within one or more FOVs (Figure 5B). The analysis based on isolated measuring points is faster than the reticular based one, but it implies a careful and, possibly, informed observation of the FOV(s) to identify the significant spectra; this means good experience dealing with pigments and painted surfaces. The reticular selection makes the algorithm time-consuming and forces the user to observe a lot of output images to retrieve a handful of useful similarity maps. However, the reticular selection provides a complete screening of the hypercubes and, mostly, it can be carried out without experience of the experimental context. In principle, once the sampling distance, n_pixel, is decided, the user can neglect the observation of the FOV(s) with a very low probability of losing details.
In addition to the selection of the reference spectra within the hypercubes, the algorithm offers the opportunity to compare the data from the sample under investigation with references belonging to other sources (step 3.2.4). The external reference spectra importer code manages the I/O of references that do not belong to the surface of the painting. The matrix converter code equalizes the wavelength ranges and the spectral resolution of both the hypercubes and the external references (step 3.2.4). This possibility extends the capabilities of the user regarding the characterization of the sample. Indeed, the user can exploit every kind of available resource in terms of reflectance data. The hypercubes can be compared with public databases, with the spectral archives of the user, with new data collected on ad hoc prepared samples or even on other objects (paintings, palettes, hues, or whatever) belonging to the author or to other artists. Moreover, the external references can be obtained exploiting any kind of reflectance techniques so much so that the references shown in Figure 6 and Figure 7 have been acquired by a portable FORS miniature spectrometer (Table of Materials) and not by the camera used for the HRI data.
Beyond the data management, the algorithm offers an original approach to the data analysis too. It allows manipulation of the spectra before evaluating the SAM maps (steps 4.1-4.5). This possibility finds its rationale in the choice of the SAM method to investigate the distributions of the pigments. In fact, SAM considers the reflectance spectra as they would be vectors in a multi-dimensional space (i.e., hyper-vectors with a number of components equal to that of the acquisition channels). Therefore, if the principal aim of the analysis is to compare different but similar references to distinguish which one best matches the pigments used by the artist, then the almost identical components of the reference spectra (i.e., the wavelengths that correspond to almost identical values in the hyper-vectors) should not be particularly useful and the algorithm allows to exclude these components from the analysis.
The protocol supports two options for manipulating the data (step 4.5): the user can define the wavelength portion(s) of the reflectance data to be analyzed manually (Figure 6) or automatically (Figure 7). The manual selection is straightforward. The pre-processed reference spectra or their first derivatives, depending on the selected pre-processing option (step 4.2), appear on an interactive window, Figure 6A, and the user selects one or more wavelength interval(s), Figure 6B, by clicking on the graph surface. The automatic selection is based on the mathematical criterion of the maximum variance applied to the pre-processed reference spectra or their first derivatives, depending on the selected pre-processing option (step 4.2). The algorithm computes the variance (normalized and displayed as a Dashed Line in Figure 7A) within the selected references and order all the spectra (both the references and the hypercubes) accordingly to this criterion (the Dashed Line in Figure 7B represents the normalized and ordered variance). In other words, if the maximum variance corresponds to the nth wavelength, the content of the nth component of each pre-processed spectrum (references and hypercubes) will be moved to the first position of a re-arranged hyper-vector and so on (the colored portions of the background in Figure 7A and Figure&#160;7B graphically explain the re-arrangement of the data). Practically, the components of the pre-processed spectra are ordered similar to principal component analysis30.
Once the spectra have been manipulated, the algorithm evaluates the SAM maps. Following the manual manipulation (Figure 6), the protocol returns three sets of maps: two corresponding to the groups of selected and rejected wavelengths and one obtained employing the whole spectra. Otherwise, following the automatic manipulation (Figure 7), the algorithm applies a floating threshold to the variance values and evaluates the SAM maps at the increasing of the&#160;threshold for both the re-arranged hyper-vector components corresponding to the over threshold (i.e., automatically selected) and to the under threshold (i.e., automatically rejected) values of the variance. These sets of maps, together with that obtained from the whole spectra (always returned by the algorithm), result in a total of (2N + 1) sets of maps where N is the number of values assumed by the threshold. The sets of similarity maps obtained at the increasing of the threshold (Figure 8) illustrate that data manipulation does not alter the content but rather provides new insights into the details of the mapped area(s) and, consequently, can help to distinguish similarities and differences between the samples and the references.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/62202/62202fig01.jpg" /> 
Figure 1: Quarto Stato. A picture of the painting, 1899-1901, 293 x 545 cm, oil on canvas, Giuseppe Pellizza da Volpedo, Museo del Novecento, Milan, Italy. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/62202/62202fig01large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/62202/62202fig02.jpg" /> 
Figure 2: Definition of the experimental conditions. (A) The ad hoc prepared test samples; the White Circles and Numbers identify the measuring points corresponding to the spectra selected as references. (B) The SAM maps evaluated with respect to reference spectrum number 1, (C) number 2, (D) number 3, and (E) number 4. The Gray Color Bar indicates the range of values of the spectral angles. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/62202/62202fig02large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/62202/62202fig03.jpg" /> 
Figure 3: The application of the defined experimental conditions to Quarto Stato. (A) The ROIs selected for the experimental campaign (Red Rectangles); in each rectangle a FOV of those necessary to cover the ROI has been highlighted (Unshaded Areas). (B) The RGB pictures of the four Unshaded Areas of panel A. (C) The SAM maps evaluated with respect to a reference spectrum selected within each FOV (Green Circles). The Gray Color Bar indicates the range of values of the spectral angles. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/62202/62202fig03large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 4" class="xfigimg" src="/files/ftp_upload/62202/62202fig04.jpg" /> 
Figure 4: Proper versus improper illumination of the surface of the sample. (A) A portion of the FOV where a small fraction of the painted surface (Red Circle) is affected by altered reflectance properties due to improper illumination. (B) The same small fraction of the painting (Blue Circle) as it results when the FOV is properly illuminated. (C) The reflectance spectra of the measuring point at the center of the circles when the FOV is improperly and properly illuminated (Red and Blue Line respectively). (D) The SAM map of the FOV obtained using the spectrum of the improper illuminated measuring point as reference. (E) The SAM map of the FOV obtained using the spectrum of the proper illuminated measuring point as reference. The Gray Color Bar refers to (D) and (E) and indicates the range of values of the spectral angles obtained comparing the first derivatives of the spectra of the hypercube of the selected FOV and the first derivative of the spectrum of the measuring point at the center of the colored circles in (A) and (B). <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/62202/62202fig04large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 5" class="xfigimg" src="/files/ftp_upload/62202/62202fig05.jpg" /> 
Figure 5: References selection within the hypercubes. (A) The isolated measuring points selection mode; the Green Circles indicate the location of the reference spectra manually selected on the FOV shown. (B) The reticular selection mode; the Green Circles indicate the location of the reference spectra selected by applying a reticulum with the sampling interval (n_pixel) set to five pixels to the FOV shown. The image reported in both (A) and (B) is the grayscale conversion of the RGB image of the FOV retrieved from the reflectance spectra applying the D65 illuminant and 1931 observer from CIE standards to the hypercube; the Gray Color Bar refers to the normalized intensity of this image. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/62202/62202fig05large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 6" class="xfigimg" src="/files/ftp_upload/62202/62202fig06.jpg" /> 
Figure 6: The manual data manipulation mode. (A) The aspect of the interactive window that allows the user to divide the reference spectra into the selected and rejected fractions of wavelengths. (B) The same references of (A) where the portions of data selected for evaluating the SAM maps have been highlighted by a pink background. (A) and (B) display the pre-processed spectra of the references. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/62202/62202fig06large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 7" class="xfigimg" src="/files/ftp_upload/62202/62202fig07.jpg" /> 
Figure 7: The automatic data manipulation mode. (A) The first derivatives of the four normalized references reported in Figure 6 (Coloured Lines) and their normalized maximum variance (Black Dashed Line). (B) The same derivatives of (A) sorted following the criterion of the maximum variance; the sorted values of the normalized maximum variance have been reported too (Black Dashed Line). Some portions of the background have been colored with different hues in the attempt to visually illustrate the re-arrangement of the hyper-vectors. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/62202/62202fig07large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 8" class="xfigimg" src="/files/ftp_upload/62202/62202fig08.jpg" /> 
Figure 8: The SAM maps obtained by the automatic data manipulation mode. (A-C) The sorted values of the normalized maximum variance evaluated within the first derivatives of the reference spectra of Figure 7; the Green and Red Sections of the curve indicate, respectively, the selected (over threshold values) and rejected fraction of the data (under threshold values). The panels show what happens at the increasing of the threshold (Black Dotted Segment); each panel reports the SAM maps for both the groups of values obtained for the four derivatives of the spectra of Figure 7; the Green Edged Maps refer to the over threshold fractions while the Red Edged Maps refer to the under threshold ones. The Gray Color Bars indicate the range of values of the spectral angles. In this example, the step that determines the increase of the threshold is equal to 0.5% of the normalized maximum variance; the threshold values reported in (C) is 0.09 and it is the last considered threshold value because a further increase would reduce the number of selected components of the hyper-vectors below the arbitrarily fixed lower limit of 20 values, i.e., 10% of the total number of the acquisition channels of the hyperspectral camera. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/62202/62202fig08large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>

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 ...

applying-hyperspectral-reflectance-imaging-to-investigate-palettes

Nanyang Technological University

Research

JoVE Journal

Chemistry

2.3K Views.  Università degli Studi di Milano-Bicocca. Dit protocol biedt nieuwe mogelijkheden met betrekking tot de analyse van schilderijen van gezichten. Het ondersteunt de gebruikers stap voor stap tijdens het hele data-analyseproces. Dit protocol heeft twee belangrijke voordelen.De gebruikers kunnen de analyse aanpassen aan hun voorkeuren en voor het eerst is spectrummanipulatie geïntroduceerd om de hyperspectrale gegevens te analyseren. Hyperspectrale reflectie beeldvorming wordt met succes gebruikt om huidziekte of tumordiagnose te...

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

Measuring Spatially- and Directionally-varying Light Scattering from Biological Material

Light interacts with an organism's integument on a variety of spatial scales. For example in an iridescent bird: nano-scale structures produce color; the milli-scale structure of barbs and barbules largely determines the directional pattern of reflected light; and through the macro-scale spatial structure of overlapping, curved feathers, these directional effects create the visual texture. Milli-scale and macro-scale effects determine where on the organism's body, and from what viewpoints and under what illumination, the iridescent colors are seen. Thus, the highly directional flash of brilliant color from the iridescent throat of a hummingbird is inadequately explained by its nano-scale structure alone and questions remain. From a given observation point, which milli-scale elements of the feather are oriented to reflect strongly? Do some species produce broader "windows" for observation of iridescence than others? These and similar questions may be asked about any organisms that have evolved a particular surface appearance for signaling, camouflage, or other reasons.
In order to study the directional patterns of light scattering from feathers, and their relationship to the bird's milli-scale morphology, we developed a protocol for measuring light scattered from biological materials using many high-resolution photographs taken with varying illumination and viewing directions. Since we measure scattered light as a function of direction, we can observe the characteristic features in the directional distribution of light scattered from that particular feather, and because barbs and barbules are resolved in our images, we can clearly attribute the directional features to these different milli-scale structures. Keeping the specimen intact preserves the gross-scale scattering behavior seen in nature. The method described here presents a generalized protocol for analyzing spatially- and directionally-varying light scattering from complex biological materials at multiple structural scales.

We present a non-destructive method for sampling spatial variation in the direction of light scattered from structurally complex materials. By keeping the material intact, we preserve gross-scale scattering behavior, while concurrently capturing fine-scale directional contributions with high-resolution imaging. Results are visualized in software at biologically-relevant positions and scales.

Light interacts with an organism's integument on a variety of spatial scales. For example in an iridescent bird: nano-scale structures produce color; the ...

Engineering

Integrated Photoacoustic Ophthalmoscopy and Spectral-domain Optical Coherence Tomography

Both the clinical diagnosis and fundamental investigation of major ocular diseases greatly benefit from various non-invasive ophthalmic imaging technologies. Existing retinal imaging modalities, such as fundus photography1, confocal scanning laser ophthalmoscopy (cSLO)2, and optical coherence tomography (OCT)3, have significant contributions in monitoring disease onsets and progressions, and developing new therapeutic strategies. However, they predominantly rely on the back-reflected photons from the retina. As a consequence, the optical absorption properties of the retina, which are usually strongly associated with retinal pathophysiology status, are inaccessible by the traditional imaging technologies.
Photoacoustic ophthalmoscopy (PAOM) is an emerging retinal imaging modality that permits the detection of the optical absorption contrasts in the eye with a high sensitivity4-7 . In PAOM nanosecond laser pulses are delivered through the pupil and scanned across the posterior eye to induce photoacoustic (PA) signals, which are detected by an unfocused ultrasonic transducer attached to the eyelid. Because of the strong optical absorption of hemoglobin and melanin, PAOM is capable of non-invasively imaging the retinal and choroidal vasculatures, and the retinal pigment epithelium (RPE) melanin at high contrasts 6,7. More importantly, based on the well-developed spectroscopic photoacoustic imaging5,8 , PAOM has the potential to map the hemoglobin oxygen saturation in retinal vessels, which can be critical in studying the physiology and pathology of several blinding diseases 9 such as diabetic retinopathy and neovascular age-related macular degeneration.
Moreover, being the only existing optical-absorption-based ophthalmic imaging modality, PAOM can be integrated with well-established clinical ophthalmic imaging techniques to achieve more comprehensive anatomic and functional evaluations of the eye based on multiple optical contrasts6,10 . In this work, we integrate PAOM and spectral-domain OCT (SD-OCT) for simultaneously in vivo retinal imaging of rat, where both optical absorption and scattering properties of the retina are revealed. The system configuration, system alignment and imaging acquisition are presented.

Photoacoustic ophthalmology (PAOM), an optical-absorption-based imaging modality, provides the complementary evaluation of the retina to the currently available ophthalmic imaging technologies. We report the using of PAOM integrated with spectral-domain optical coherence tomography (SD-OCT) for simultaneous multimodal retinal imaging in rats.

Both the clinical diagnosis and fundamental investigation of major ocular diseases greatly benefit from various non-invasive ophthalmic imaging ...

Bioengineering

RGB and Spectral Root Imaging for Plant Phenotyping and Physiological Research: Experimental Setup and Imaging Protocols

Better understanding of plant root dynamics is essential to improve resource use efficiency of agricultural systems and increase the resistance of crop cultivars against environmental stresses. An experimental protocol is presented for RGB and hyperspectral imaging of root systems. The approach uses rhizoboxes where plants grow in natural soil over a longer time to observe fully developed root systems. Experimental settings are exemplified for assessing rhizobox plants under water stress and studying the role of roots. An RGB imaging setup is described for cheap and quick quantification of root development over time. Hyperspectral imaging improves root segmentation from the soil background compared to RGB color based thresholding. The particular strength of hyperspectral imaging is the acquisition of chemometric information on the root-soil system for functional understanding. This is demonstrated with high resolution water content mapping. Spectral imaging however is more complex in image acquisition, processing and analysis compared to the RGB approach. A combination of both methods can optimize a comprehensive assessment of the root system. Application examples integrating root and aboveground traits are given for the context of plant phenotyping and plant physiological research. Further improvement of root imaging can be obtained by optimizing RGB image quality with better illumination using different light sources and by extension of image analysis methods to infer on root zone properties from spectral data.

An experimental protocol is presented for assessment of soil grown plant root systems with RGB and hyperspectral imaging. Combination of RGB image time series with chemometric information from hyperspectral scans optimizes insights into plant root dynamics.

Better understanding of plant root dynamics is essential to improve resource use efficiency of agricultural systems and increase the resistance of crop ...

Environment

Visualizing Visual Adaptation

Many techniques have been developed to visualize how an image would appear to an individual with a different visual sensitivity: e.g., because of optical or age differences, or a color deficiency or disease. This protocol describes a technique for incorporating sensory adaptation into the simulations. The protocol is illustrated with the example of color vision, but is generally applicable to any form of visual adaptation. The protocol uses a simple model of human color vision based on standard and plausible assumptions about the retinal and cortical mechanisms encoding color and how these adjust their sensitivity to both the average color and range of color in the prevailing stimulus. The gains of the mechanisms are adapted so that their mean response under one context is equated for a different context. The simulations help reveal the theoretical limits of adaptation and generate &#34;adapted images&#34; that are optimally matched to a specific environment or observer. They also provide a common metric for exploring the effects of adaptation within different observers or different environments. Characterizing visual perception and performance with these images provides a novel tool for studying the functions and consequences of long-term adaptation in vision or other sensory systems.

This article describes a novel method for simulating and studying adaptation in the visual system.

Many techniques have been developed to visualize how an image would appear to an individual with a different visual sensitivity: e.g., because of optical ...

Behavior

Visualization of Ambient Mass Spectrometry with the Use of Schlieren Photography

This manuscript outlines how to visualize mass spectrometry ambient ionization sources using schlieren photography. In order to properly optimize the mass spectrometer, it is necessary to characterize and understand the physical principles of the source. Most commercial ambient ionization sources utilize jets of nitrogen, helium, or atmospheric air to facilitate the ionization of the analyte. As a consequence, schlieren photography can be used to visualize the gas streams by exploiting the differences in refractive index between the streams and ambient air for visualization in real time. The basic setup requires a camera, mirror, flashlight, and razor blade. When properly configured, a real time image of the source is observed by watching its reflection. This allows for insight into the mechanism of action in the source, and pathways to its optimization can be elucidated. Light is shed on an otherwise invisible situation.

This paper presents a protocol for the visualization of gaseous streams of an ambient ionization source using schlieren photography and mass spectrometry.

This manuscript outlines how to visualize mass spectrometry ambient ionization sources using schlieren photography. In order to properly optimize the mass ...

Examining Changes in HRV and Emotion Following Artmaking with Three Different Art Materials

This protocol enables the examination of psychological and physiological responses to different types of behavioral engagements. Specifically, in this study example, the emotional response and changes in heart rate variability are examined in response to artmaking with three different art materials that vary in their levels of fluidity. This protocol can be adapted to examine other types of behavior or engagement in artmaking with other materials. There are several benefits to using this protocol. Firstly, the order randomization of the materials improves the probability that the response measured is associated with its qualities and not the order of presentation. Secondly, the continuous measuring of electrocardiogram enables the examination of changes in heart rate variability after engagement with each art material and changes that might occur during the artmaking itself. The advantages of this protocol should be considered with their limitations. The music listening is before each art making session; thus, the return to baseline can only be measured in the first two conditions. The return to baseline provides information on how fast individuals recover after response to working with each of the materials. Furthermore, a more liquid material instead of gouache paint with a brush, such as finger paints, provides more difference between materials. Finally, this protocol can be adapted to specific research needs.

The goal of the protocol is to guide researchers in conducting experiments that are intended to measure changes in self-reported emotional response and heart rate variability following art making with different materials. The protocol can easily be adapted for use in a variety of behavioral conditions and activities.

This protocol enables the examination of psychological and physiological responses to different types of behavioral engagements. Specifically, in this ...

Excitation-Scanning Hyperspectral Imaging Microscopy to Efficiently Discriminate Fluorescence Signals

Several techniques rely on detection of fluorescence signals to identify or study phenomena or to elucidate functions. Separation of these fluorescence signals were proven cumbersome until the advent of hyperspectral imaging, in which fluorescence sources can be separated from each other as well as from background signals and autofluorescence (given knowledge of their spectral signatures). However, traditional, emission-scanning hyperspectral imaging suffers from slow acquisition times and low signal-to-noise ratios due to the necessary filtering of both excitation and emission light. It has been previously shown that excitation-scanning hyperspectral imaging reduces the necessary acquisition time while simultaneously increasing the signal-to-noise ratio of acquired data. Using commercially available equipment, this protocol describes how to assemble, calibrate, and use an excitation-scanning hyperspectral imaging microscopy system for separation of signals from several fluorescence sources in a single sample. While highly applicable to microscopic imaging of cells and tissues, this technique may also be useful for any type of experiment utilizing fluorescence in which it is possible to vary excitation wavelengths, including but not limited to: chemical imaging, environmental applications, eye care, food science, forensic science, medical science, and mineralogy.

Spectral imaging has become a reliable solution for identification and separation of multiple fluorescence signals in a single sample and can readily distinguish signals of interest from background or autofluorescence. Excitation-scanning hyperspectral imaging improves on this technique by decreasing the necessary image acquisition time while simultaneously increasing the signal-to-noise ratio.

Several techniques rely on detection of fluorescence signals to identify or study phenomena or to elucidate functions. Separation of these fluorescence ...

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 molecular single crystals. As representative example, we chose a single crystal of the heterodinuclear Ln-based complex [TbEu(bpm)(tfaa)6] (bpm=2,2&#8217;-bipyrimidine, tfaa&#8211; =1,1,1-trifluoroacetylacetonate) exhibiting bright visible emission under UV excitation. HSI is an emerging technique that combines 2-dimensional spatial imaging of a luminescent structure with spectral information from each pixel of the obtained image. Specifically, HSI on single crystals of the [Tb-Eu] complex provided local spectral information unveiling variation of the luminescence intensity at different points along the studied crystals. These changes were attributed to the optical anisotropy present in the crystal, which results from the different molecular packing of Ln3+ ions in each one of the directions of the crystal structure. The HSI herein described is an example of the suitability of such technique for spectro-spatial investigations of molecular materials. Yet, importantly, this protocol can be easily extended for other types of luminescent materials (such as micron-sized molecular crystals, inorganic microparticles, nanoparticles in biological tissues, or labelled cells, among others), opening many possibilities for deeper investigation of structure-property relationships. Ultimately, such investigations will provide knowledge to be leveraged into the engineering of advanced materials for a wide range of applications from bioimaging to technological applications, such as waveguides or optoelectronic devices.

Here, we present a protocol to obtain luminescent hyperspectral imaging data and to analyze optical anisotropy features of lanthanide-based single crystals using a Hyperspectral Imaging System.

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

Multimodal Imaging and Spectroscopy Fiber-bundle Microendoscopy Platform for Non-invasive, In Vivo Tissue Analysis

Recent fiber-bundle microendoscopy techniques enable non-invasive analysis of in vivo tissue using either imaging techniques or a combination of spectroscopy techniques. Combining imaging and spectroscopy techniques into a single optical probe may provide a more complete analysis of tissue health. In this article, two dissimilar modalities are combined, high-resolution fluorescence microendoscopy imaging and diffuse reflectance spectroscopy, into a single optical probe. High-resolution fluorescence microendoscopy imaging is a technique used to visualize apical tissue micro-architecture and, although mostly a qualitative technique, has demonstrated effective real-time differentiation between neoplastic and non-neoplastic tissue. Diffuse reflectance spectroscopy is a technique which can extract tissue physiological parameters including local hemoglobin concentration, melanin concentration, and oxygen saturation. This article describes the specifications required to construct the fiber-optic probe, how to build the instrumentation, and then demonstrates the technique on in vivo human skin. This work revealed that tissue micro-architecture, specifically apical skin keratinocytes, can be co-registered with its associated physiological parameters. The instrumentation and fiber-bundle probe presented here can be optimized as either a handheld or endoscopically-compatible device for use in a variety of organ systems. Additional clinical research is needed to test the viability of this technique for different epithelial disease states.

Multimodal Imaging and Spectroscopy Fiber-bundle Microendoscopy Platform for Non-invasive, In Vivo Tissue Analysis

The assembly and use of a multimodal microendoscope is described which can co-register superficial tissue image data with tissue physiological parameters including hemoglobin concentration, melanin concentration, and oxygen saturation. This technique can be useful for evaluating tissue structure and perfusion, and can be optimized for individual needs of the investigator.

Recent fiber-bundle microendoscopy techniques enable non-invasive analysis of in vivo tissue using either imaging techniques or a combination of ...

Simultaneous Evaluation of Cerebral Hemodynamics and Light Scattering Properties of the In Vivo Rat Brain Using Multispectral Diffuse Reflectance Imaging

The simultaneous evaluation of cerebral hemodynamics and the light scattering properties of in vivo rat brain tissue is demonstrated using a conventional multispectral diffuse reflectance imaging system. This system is constructed from a broadband white light source, a motorized filter wheel with a set of narrowband interference filters, a light guide, a collecting lens, a video zoom lens, and a monochromatic charged-coupled device (CCD) camera. An ellipsoidal cranial window is made in the skull bone of a rat under isoflurane anesthesia to capture in vivo multispectral diffuse reflectance images of the cortical surface. Regulation of the fraction of inspired oxygen using a gas mixture device enables the induction of different respiratory states such as normoxia, hyperoxia, and anoxia. A Monte Carlo simulation-based multiple regression analysis for the measured multispectral diffuse reflectance images at nine wavelengths (500, 520, 540, 560, 570, 580, 600, 730, and 760 nm) is then performed to visualize the two-dimensional maps of hemodynamics and the light scattering properties of the in vivo rat brain.

Simultaneous Evaluation of Cerebral Hemodynamics and Light Scattering Properties of the In Vivo Rat Brain Using Multispectral Diffuse Reflectance Imaging

The simultaneous evaluation of cerebral hemodynamics and the light scattering properties of in vivo rat brain tissue is demonstrated using a conventional multispectral diffuse reflectance imaging system.

The simultaneous evaluation of cerebral hemodynamics and the light scattering properties of in vivo rat brain tissue is demonstrated using a conventional ...

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

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