Revealing time’s secrets at the National Theatre of Costa Rica via innovative software for cultural heritage research

Establishing affordable, efficient, accessible, innovative, and multidisciplinary methodologies to the diagnosis of the conservation state of an artwork is key to carry out appropriate strategies of conservation and consequently to the creation of modern public policies on cultural heritage. Limited access to large-format paintings is a challenge to restoration scientists seeking to obtain information quickly, in a non-destructive and non-invasive manner, and identify regions of interest. Therefore, we put forward two unique software tools based on multispectral imaging techniques, with the long-term aim to assess the artist’s intentions, creative process, and colour palette. This development paves the way for a comprehensive and multidisciplinary understanding of the mysteries encompassed in each pictorial layer, through the study of their physical and chemical characteristics. We conducted the first ever study on Musas I and Musas II, two large-format paintings by Italian artist Carlo Ferrario, located in the National Theatre of Costa Rica. In this study, we used our novel imaging techniques to choose regions of interest in order to study sample layers; while also assessing the works’ state of conservation and possible biodeterioration. We explored the applications of our two versatile software tools, RegionOfInterest and CrystalDistribution, and confirmed paint stratigraphies by means of microscopy and spectroscopy analyses (OM, SEM-EDX, Fluorescent microscopy, FTIR-ATR and micro-Raman). In a pilot study, we identified the artist’s main colour palette: zinc white, lead white, chrome yellow, lead read, viridian, along with artificial vermilion and ultramarine pigments. We were able to identify artificial vermilion and ultramarine and distinguish them from the natural pigments using CrystalDistribution to map the average size and diameter of the pigment crystals within the paint layers. This study demonstrated that software-based multidisciplinary imaging techniques are novel in establishing preventive and non-invasive methods for historical painting conservation studies, in addition, this study provides tools with great potential to be used in the future in applications such as virtual restoration.

have been previously restored, however there are no descriptions or documentation of the type of work performed. In previous studies of artwork at the NTCR, we were able to examine only a limited number of samples, without first identifying areas of interest 17,18 . This is the first time we have had the opportunity to take a comprehensive, multidisciplinary approach.
Tropical countries raise different challenges about the conservation and restoration of paintings. Art pieces imported into Latin America were not designed to withstand the effects of the temperatures and humidity of the tropics. There are many studies on the conservation and restoration of large-format artworks, involving the techniques discussed above, but they took place in Europe 1, 19-22 and did not address tropical factors. There are even fewer restoration protocols suited to the works in question. Deterioration caused by tropical conditions is still largely unstudied, underscoring the importance of the present investigation.
This article describes the study of paintings Musas I and Musas II through a multi-analysis approach that includes: (1) MSI coupled with novel computational tools for image analysis, (2) microscopy and spectroscopy analyses with the development of custom software to identify the size of pigment crystals observed in stratigraphies of the samples, (3) microbiological analysis for fungi identification in previously defined regions of interest, and (4) monitoring of environmental conditions in the paintings' surroundings, in order to present a general diagnostic of the paintings.

Results
Carlo Ferrario painted Musas I and Musas II 123 years ago. Today, we contemplate our heritage and wonder: what history is woven into these paintings? What secrets lurk in each brushstroke, pigment, fiber and crack in the canvas? Why did the artist choose each material? Figure 1 symbolizes our multi-disciplinary approach to these questions, as we draw on diverse fields of study, tools and techniques to better understand the paintings' history and assess their current state of conservation. It is important to note that restoring large-format paintings does not seek to turn back time or erase history 23 , but rather to preserve it for future generations.

More than meets the eye: Multispectral imaging
Multispectral imaging (MSI) reveals information difficult or impossible to see with the naked eye, especially useful with large-format paintings, where exhaustive in-person examination may not be practical. (bottom) show inner layers of the paintings, because some external layers do not absorb infrared radiation, rendering them transparent 1 . Other materials appear dark, such as the dress of the woman holding the Earth in Musas I, see Fig. 2b. Pigments in this area absorb infrared radiation 24 , while the reflective pigments opposite them look brighter 24 . IRFC images combine channels from both VIS and infrared images, revealing particle pigment behaviours.
UVF shows the fluorescence of each material. Varnish usually fluoresces strongly, overshadowing pigment fluorescence 25 . This effect is not observed in Musas I and Musas II (Fig. 2d (top) and Fig. 2d (bottom)) because the varnish was removed with ammonia in 1997, according to NTCR reports. The UVF pictures also show: the exposed canvas; brighter areas of the clouds may indicate the use of different binders; stains caused by insects or microorganisms; and possible areas of restoration, such as the white cloud behind the head of the main muse on Fig. 2d (top). Meanwhile, UVR ( Fig. 2e (top) and Fig. 2e (bottom)) provides information on the outermost layers 24 and biological staining. UVR was used primarily to identify white pigments 1, 2 . Titanium and zinc white appear dark, while lead and lithopone appear bright 2 , so we can infer that the bright regions in Fig. 2e (top) and Fig. 2e (bottom) correspond to lead white or lithopone.
Deeper secrets in heritage research: developing software for closer analysis Further MSI analyses are needed to reveal underlying layers, sketches and corrections, possible previous restoration and other regions of interest. We believe software based on multispectral imaging can offer a new perspective in the study of art. We developed RegionsOfInterest to classify colour luminosity, showing chromatic distributions in large-format works. This is relevant to art history, art restoration, and even an artist's creative process. Figure 3 illustrates the RegionsOfInterest tool, developed specially for restoration scientists:

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• The user selects a sample area. The colours from this area will be mapped to the artwork as a whole, in order to determine the spatial distribution of areas of interest such as colour, pigment and other zones (see Fig. 3b and Fig. 3c).
• The histogram in Fig. 3d shows this region's intensity values.
• RegionsOfInterest displays regions in the specified intensity range on the artwork as a whole, see Fig. 3f.
• Lastly, Fig. 3g shows an increase in the number of pixel intensity counts for this region.
The software can be used to systematically select regions of interest for comprehensive and efficient non-invasive sampling, for conservation diagnostics. This novel and time-saving tool has three applications:

Comparing colour intensity behaviours
Firstly, we characterized a range of whites, which appear to be the most abundant colour in the artwork. Analysis of 11 regions in Musas I using RegionsOfInterest resulted in a pixel intensity value of approximately 132 ±12. Since similar values are obtained for white regions containing similar compounds, intensity analysis software is a useful tool during the composition stage of the creative process. For instance, the artist can establish a more complete and complex colour palette based on colour saturation by analyzing colour intensity distribution with RegionsOfInterest.

Revealing the colour palette
Secondly, following visual analysis of the painting and colour palettes prevalent at the time, we hypothesized that all colours in the paintings were obtained by mixing approximately 12 original pigments. As a proof-of-principle, we analyzed the paintings with the RegionsOfInterest program to identify colour distributions that could provide insight into the artist's palette. We examined six colour intensities (yellow, red, blue, green, brown, and white). On average, yellow was the most prevalent, at about 20% abundance, while green was the least, at only 1%. The single most striking observation was that these colour intensities make up 50% of Musas I and 30% of Musas II. This application could be useful in other case-studies, to identify an artist's main palette and the agents responsible for deterioration.  27 . This compound was widely used as a ground material during the nineteenth century 26 . We were also interested in a unique morphology of foraminifera: prehistoric marine invertebrates which turned out to be an important source of calcium carbonate, CaCO 3 26 . Additionally, Fig. 4c shows fluorescent particles observed through inverted fluorescence microscopy, discussed in the forthcoming section. The elemental composition of each layer identified by SEM-EDX is shown in Fig. 4d and Fig. 4e, respectively. We found that, generally, the top paint layer contains an important amount of zinc; layers 2 and 3 contain mostly lead; and the first ground consists of calcium from calcium carbonate, as expected. As we found about 44% of zinc in some regions of the ground layer, we hypothesize that the ground contains zinc white pigment mixed with calcium carbonate.
To the best of our knowledge, there has been no systematic study of the colour palette used by Carlo Ferrario. In order to contextualize his creative process, we studied the pigments from both the pictorial and ground layers. The density of crystals in an area of approximately 2000 µm 2 was arbitrarily categorized as follows: fewer than 4 crystals was considered low density, 4-20 crystals medium density, and more than 20 crystals high density. The average diameter of the crystals was calculated using our custom CrystalDistribution software, see Fig. 5, which shows how each colour was analysed individually to determine crystal size and quantity. We employed micro-Raman spectroscopy to identify the chemical composition of these pigments, see Fig. 6a. The measured spectra were compared to reference pigments from the Cultural Heritage Open Source Pigments Checker (CHSOS).

Lead red
Lead red pigment in Musas I was likely used in areas that appear light red, such as the cloak and other reddish-pink clothing and ornaments. Microscopically, large light red with some orange hue crystals were found in the paint layer of sample M1-54P which is located in one of the lighters sections of the mantle, see Figure 2g (top). The cross-section of 4/18 this sample shows a low density of crystals with an average diameter of about 21 µm, see Fig. 6b. Lead red, applied over other base colours, such as vermilion, has been commonly used since the middle-ages to give the effect of silk cloth 28 . Ferrario may well have used this technique. Figure 6a presents Raman spectra of this crystal, showing characteristic components of lead red, in particular the Pb-O vibrational modes 29 at 550 cm −1 .

Viridian
Viridian pigment could be found in green tones in the mountains, background and forest area. Interestingly, among all the samples under study, only M2-69P shows green crystals in its paint stratigraphy, see Fig. 6c. Low density of these crystals was observed in the paint layer, with an average diameter of roughly 5.1 µm, see Fig. 5c. Michelangelo used an underlayer of green earth and viridian to create the effect of light 30 . Ferrario may have used the related verdaccio method, in which a neutral colour, usually green, is applied in underpainting for outlining and shading 31 . Raman analyses identified the green crystals as viridian, with a reference signal at 538 cm −1 for chrome III oxide, Cr 2 O 3 32 , see Fig. 6a. However, the samples' spectra showed a signal at 479 cm −1 generated by the Cr (V I) -O stretching mode 33 of dihydrated chrome III oxide Cr 2 O 3 ·2H 2 O, the major component of viridian. Developed in 1838 34 , this pigment was very expensive until a French chemist devised an alternate production method in 1859, making it more accessible to contemporary artists 34 .

Ultramarine
Blue pigment is most evident in the sky areas of both Musas I and Musas II as well as a glimpse of blueish highlights in the mountain areas. Most of the samples contain low density of blue crystals. Figure 2f (top) shows the sample with the bluest colour, dark violet, with medium density of blue crystals in layers 1 and 2. The average diameter of these crystals is approximately 2.0 µm, see Fig. 5c. The average size of the crystals indicates that Ferrario most likely worked with synthetic low-cost ultramarine rather than the expensive natural pigment. As expected, the Raman spectra for these crystals coincides with the spectra of both natural and artificial ultramarine pigments of the CHSOS, see Fig. 6a. The mineral lazurite is an aluminosilicate of approximate formula (Na,Ca) 8 (AlSiO 4 ) 6 (SO 4 ,S,Cl) 2 35 , which is often associated with other silicate minerals like calcite (CaCO 3 ) and pirite (FeS 2 ) 36 . The spectra of natural and synthetic reference pigments are not different enough to distinguish them because both have bands of the S − 3 ion at 548 cm −1 (symmetric stretching vibration), 258 cm −1 (bending vibration) and 1096 cm −1 (stretching vibration) 35 . What is interesting in this data is that our CrystalDistribution software identified uniform particle distribution in both size and roundness, as reported in recent studies 35 , revealing the pigment to be artificial ultramarine with its smaller crystal size (0.5-5.0) µm 36 .

Vermilion
The purest and most saturated red, seen in the book in Musas II, is likely vermilion red. The same pigment may have been washed or mixed with other colours to create the pinkish tones in the textiles. In fact, red crystals were found in samples of light brown, pink and red areas. The redder the colour, the higher the density of crystals in the cross-section. For instance, in samples M1-54P and M2-69P, red crystals of approximately 1.4 µm and 3.0 µm, respectively, are found in low density in the paint layer, see Fig. 5c. It is well-known that Italian schools commonly used a triad of vermilion, greenish-yellow and violet-blue to achieve chromatic equivalents 37 . Another reliable technique involving vermilion was to underpaint thin transparent touches of vermilion with another white pigment to achieve a pink tone for the rosy and ruddy portions of flesh and skin 38 . Ferrario likely used both of these techniques. Raman spectra of red crystals were associated with natural and artificial vermilion pigments due to similar composition of mercury (II) sulfide, with characteristic signals at 251 cm −1 and 345 cm −120 (see Fig. 6a and Fig. 6d). The particle size, fineness and uniformity of the vermilion crystals suggests the presence of artificial vermilion in these samples 36 . It is interesting to note that even though vermilion itself does not fluoresce, the UVF picture of Fig. 2d (top) shows slight pink fluorescence in the red mantle. This pigment can exhibit visible fluorescence induced by UV light due to the organic binder, if mixed with lead white or exposed to ammonia 39 .

Chrome yellow
Yellow colours can be seen in elements like the harp in Musas I, and ornaments including clothing details in both paintings. Yellow pigments washed or mixed with other colours is subtly perceptible in clouds, highlights, hair and skin tones. Chrome yellow was commonly used in artworks of the period. Yellow crystals at different density levels were found in all samples observed through OM. For instance, sample M1-75W has high density of yellow and orange crystals in the paint layer, see Fig. 4b. We measured average crystal diameter of around 7.7 µm, see Fig. 5c. Chrome yellow provided artists with a heavy saturated yellow pigment that balanced out other intense colours like red and blue. Ferrario may have chosen this pigment due to its reliability and popularity in the nineteenth and early twentieth centuries. The use of this pigment may have caused parts of the paintings to darken over time. Raman spectra show the characteristic bands of chrome yellow, also obtained for the CHSOS reference pigment. Chrome yellow consists of lead chromate

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with the following chemical formula: PbCrO 4 28 along with Raman signal around 842 cm −1 caused by the Cr (V I) -O stretching 32 , see Fig. 6a and Fig. 6d. A similar spectrum was obtained for the orange crystals observed in the samples. However, instead of the signal at 842 cm −1 , a doublet is observed at 828 cm −1 and 848 cm −1 . Likewise, the observed signal at 361 cm −1 of the chrome yellow becomes four signals within roughly 337 cm −1 . The latter is caused by the presence of oxidized lead (II) chromate, PbCrO 4 .Pb(OH) 2 , which is usually called chrome orange 40 . We hypothesize that the presence of this compound is evidence of chrome yellow degradation, due to its sensitivity to light 28 . Chrome yellow was introduced as a pigment in 1804 28 and production increased in 1820 41 . It was rather expensive in the first half of the nineteenth century 34 , but the price had come down by the time Ferrario was working on Musas I and Musas II. Nowadays the pigment is seldom used due to its dangerous toxicity 28 .

Zinc and lead white
White is a predominant colour in both Musas I and Musas II, most apparent in the clouds in both compositions. Lead white was the main white pigment on the market in Ferrario's time, although zinc white was being developed as a safer alternative 42 . The fluorescent microscope revealed medium density of fluorescent particles in the first ground layer, see Fig. 4c. However, in the optical microscope image, the fluorescent particles are indistinguishable from the rest of the components of the ground. Therefore, a white pigment with fluorescence characteristics 24 , such as zinc white, must have been used in the preparation of the ground layer 43 . The average diameter calculated for those particles was 5 µm. The evidence that zinc white may be present includes its cold, flat tone 34 , poor oil drying and low pigment density 42 . Manufacturers began adding zinc white to other pigments as a lightening agent 28 . Although zinc white was not a very popular pigment, a few artists were fond of it 34 . Ferrario may have been one of them, but there is evidence that he might have used a mixture of zinc white with another white, or a pigment mixed with zinc white. Micro-Raman analysis was performed on these fluorescent particles. The spectra obtained allowed us to identify zinc white, comparing it to the CHSOS reference pigment. The Raman spectra correspond to bands at approximately 447 cm −1 and 610 cm −130 which agrees with the fluorescence microscopy because ZnO fluoresces when exposed to ultraviolet light 43 . As mentioned above, the second ground layer is homogeneous white, without coloured pigments. Measurements using SEM-EDX taken on a specific region reveal the following percentages: 27% of carbon, 14% of oxygen, 2% of calcium, 53% of lead and 4% of zinc. This significant amount of lead, carbon and oxygen could indicate the presence of lead white pigment (PbCO 3 ) 2 ·Pb(OH) 2 44 . We recommend additional studies to corroborate this hypothesis. Zinc white was developed to reduce the incidence of lead poisoning 28,34 . However, at 8 French francs per pound, it was four times the cost of lead white 28 , so in many cases artists were slow to adopt the newer product.

Mapping of damage
Thirdly, intensity analysis can be a first step towards identifying deterioration agents and selecting possible restoration techniques. Multispectral Imaging (MSI), combined with RegionsOfInterest, shows the type and severity of damage on artwork, allowing us to select areas of interest with visual atypical behaviours. The main observations are shown in Fig. 7. Panels Fig. 7a and Fig. 7b show the paintings themselves. Panels Fig. 7a.6 and Fig. 7b.6, show a schematic of the paintings for easier reference. We identified ten categories of damage: 1) moisture marks Fig. 7b.2, 2) detachment of canvas (bubbles) Fig. 7b.1, 3) cracks Fig. 7b.3, 4)  Moisture marks are among the most prevalent types of damage 45 caused by leaks, dampness or floods which can weaken and detaches the adhesive layer bonding the canvas to the wood support 45 . Possible effects include bulges or air bubbles as observed in Fig. 7b.1) and even cracks, Fig. 7b.3). The glue binding the ground to the canvas may detach in relative humidity above 80 %. As the canvas shrinks paint layers may separate, leaving the canvas exposed, Fig. 7a.1. Craquelure may also cause paint loss 46 . This cracking in the paint layer reflects the materials and technique used by the artist and its pattern can even provide information on the creative process 46 . The location of the craquelure on Musas I and Musas II suggests it was caused by moisture inside the paintings.
Three predominant types of deterioration on Musas I and Musas II are: moisture marks Fig. 7b.2, craquelure Fig. 7a.3, and loss of paint and exposed canvas Fig. 7a.1. We also observed human-caused damage, such as fingerprints Fig. 7b with an average of 70.3 %, see Fig. 8e. These conditions are considered very high-risk for biodeterioration. Air pollution and radiation levels are also known to contribute to the deterioration process [49][50][51][52] ; ergo, we recommend monitoring these parameters in the future. Fungi can cause severe aesthetic and structural deterioration, since composite materials provide a number of substrates for microbial growth 53 . Most fungi penetrate the fiber lumen where they grow a mycelium 54,55 , and some have been found to secrete cellulolytic enzymes that dissolve cellulose fibers, causing loss of strength, elasticity, structure, and ultimately damaging the canvas 56 . Additionally, a number of fungi and bacteria are known to produce pigments or discoloration on artwork 57,58 . Identifying possible areas of biodeterioration with MSI-coupled software tools in a great advantage, since fungal proliferation is often only noted once it has caused significant damage 59 .

Conclusions
This research aimed to provide a baseline diagnostic of the conservation state of the paintings Musas I and Musas II using comprehensive and multidisciplinary methodologies. In particular, this multi-analytical study (MAS) focused on the challenges of painting conservation in a tropical climate, and sought to provide fundamental knowledge to prevent and slow down this decay. Novel software allowed us to gather data in a non-destructive manner. One of the most significant outcomes of this study, made possible by our RegionsOfInterest software, was the identification of areas of particular interest and assessment of the artist's colour palette through minimally invasive sampling. We found that zinc white, lead white, chrome yellow, lead red, viridian, vermilion, and ultramarine blue pigments make up approximately 50% of the total pigment composition in the artwork, demonstrating one of the ways this innovative technology can be applied to conservation, restoration and even art history research on large-format paintings. Our second major development was the CrystalDistribution software, which scans sample cross-sections and calculates pigment crystal number and size distributions. Using this tool, we were able to identify synthetic vermilion and ultramarine blue in the paintings, as opposed to natural pigments. This finding has important implications in developing affordable, efficient, accessible, and innovative non-destructive laboratory analyses, especially given the current limited access to cultural heritage sites. The third significant finding of this study was the ability to map damaged areas on the works, using RegionsOfInterest to guide future restoration efforts. The results in this study have demonstrated the importance of a MAS approach to large-format paintings and the value of our two novel software tools for art conservation studies. In further research, we believe that further software tools could be developed to reveal the artist's intentions and creative process, for example, using machine learning techniques similar to deep neural networks 60 .

Materials and Experimental Multispectral Imaging: photography acquisition
We generated visible (VIS), infrared (IR), infrared false colour (IRFC) and ultraviolet (UV) photographs. VIS photos show possible superficial damage and suggest the colours used by the artist, which may have changed with age or restoration 61 . IR imaging shows background layers; such as underlying sketches, drawings and corrections by the artist 62,63 . UV imaging shows deeper damage and compounds that fluoresce when excited with UV radiation, like certain pigments, binders and glues 63,64 .
Photography acquisition was based on the procedure described by Cosentino (2014) 24 . Due to the large dimensions of the paintings (2.97 m (w), 6.17 m (h)), a set of 30 pictures was obtained for each spectral region: VIS, IR, ultraviolet fluorescence (UVF) and ultraviolet reflectance (UVR). Since the artworks are located on the ceiling, the photographic equipment was placed on a grid marked on the floor roughly 3.0 m below. Both sets of pictures were obtained using a modified camera (Nikon D7200) with the following filters: XNiteCC1 66 used for the white balance was placed on one border of the painting. We used Cultural Heritage Open Source Pigments Checker (CHSOS) pigment reference for the photos in every region; and Lightroom Classic® and Photoshop® for lens corrections and generation of Infrared False colour (IRFC) pictures, respectively. We generated panoramic images in each spectral region using PTGUI® Software. For IRFC we used Adobe Photoshop® to overlay VIS and IR images. Both must align precisely to exchange their red, green and blue channels as follows: in VIS the blue channel was suppressed and substituted with the green one, then the red channel substituted the green channel, and the IR red replaced the VIS ex-red channel 61, 62 .

Software development
Given the importance of digital image processing in painting analysis 67, 68 , we developed two novel software tools, RegionOfInterest and CrystalDistribution, written in Python3 using the OpenCV library 69,70 for pixel colour management in the HSV (Hue-Saturation-Value) colour space 71 . Both are available for free at the GitHub repository https://github.com/andress5990/16 _Intensity_Analysis and are open to modification and improvements.

RegionOfInterest program for luminosity analysis of paintings
RegionOfInterest has two stages to quantify pixel intensity value. The first takes a user-created, hand-drawn image region of any shape and size. The second separates all pixels in the image with an intensity value within a user-selected range based on the stage one results. These two execution routines use the OpenCV colour to gray transformation and classify pixels in a range from 0 (black or null intensity) to 255 (white or full intensity) 71,72 . The program then generates histograms of "Count vs intensity", according to the distribution of the intensity values. The second stage generates a colour image containing only pixels with intensity values within the selected range, showing their location on the original image. Intensity measurement uses the OpenCV cv2.colour_BGR2GRAY library function 68,72 . It was compared with BT.601 and BT.709 colour to gray transformation methods 73,74 , with uncertainties of ±0.2 % and ±2.1 % respectively. Program execution time was approximately 1 s. It is important to note that processing time depends on image format and size and computer processing power. These analyses are also heavily dependent on image quality and lighting conditions.

CrystalDistribution software tool to measure pigment crystals
CrystalDistribution identifies the number, diameter and area of crystals of a selected colour in 50x magnified images. A code change is required to use this software on other magnifications; this particular feature is not user-selectable. The code has three functions: (1) image analysis, (2) numerical calculations, and (3) crystal counting. The first consists of slicing a microscopic image. User made cuts allow both specific region and layer by layer analyses. We established hue, saturation and component ranges to create a mask for each colour layer. Each mask is applied to the cuts and saved as an image. We then calculate the area of the crystals by applying a mask of the corresponding colour to identify their contour, and finding their center. This information is used to generate "Crystal count vs diameter" histograms (in ranges of 2 µm from 0 µm to 25 µ m).

Sampling along with microscopy and spectroscopy analysis
Under the supervision of the Manager of the NTCR Conservation Department we collected 15-millimetric samples (0.5 mm 2 -3 mm 2 ) with a scalpel from already-damaged regions, so as to minimize risk of further damage. Table 1 presents an overview of the main characteristics of these samples. We used a scaffold, since the paintings are located on the ceiling. Figure 2f (top) and Figure 2f (bottom) show the grid for sample collection and labeling. To set up the samples, cross-sections were embedded in epoxy resin (Fibrocentro®) and polished with water sanding sheets (3M®) of 9.2 µm, 6.5 µm 3 µm and 1 µm grit. Samples were sanded for approximately 3 minutes with each sheet, in descending order. We then used a polisher (EcoMet 30, Buehler®) for two 10-minute cycles with 15 µm and 3 µm alumina abrasive (Allied High Tech Products®), and the following conditions: 150 rpm for stage and 150 rpm and 40 N force for the support arm, which rotated opposite to the stage. Cross-sections were examined by Optical Microscope, SEM-EDX and Raman.

Optical microscopy
Cross-sections were examined with optical microscopy at 5x, 10x, 20x and 50x magnifications, using a Nikon Eclipse LV100ND light microscope equipped with a Nikon DS-fi3 digital camera. Microscopic images with reflected light reveal the paintings' stratigraphy and the colour of individual pigment grains 5 . 10x magnification showed that cross-sections were composed typically of 2 to 4 layers, see Fig. 4b. At 20x and 50x magnifications we observed pigment crystals in the painting layer ranging from about 3 µm to 18 µm, and fossils of 20 µm to 50 µm in the ground layer. Fluorescent crystals in the ground layer were observed through Leica DMi8 Invert fluorescence microscope and images were capture at 5X, 10X and 100X. The signal was measured with a DAPI LP filter, with a range of (420 ± 20) nm for the excitation band pass filter and a range of (457 ± 20) nm for the suppression band pass filter.

Scanning Electron Microscope -Energy Dispersive X Ray Spectroscopy (SEM-EDX)
We measured the paint stratigraphy of 8 samples with EDX to determine elemental composition of the layers. Mapping and single point analysis was conducted using a scanning electron microscope HITACHI S-3700n coupled with an IXRF Systems Detector Energy-Dispersive X-ray spectrometer. The samples were uncoated with gold and analysed in low vacuum conditions and backscattering electron mode, under the following analytical conditions: 15 kV accelerating voltage, 80 µA beam current, working distance ranging from about 5.5 mm to 10.9 mm and collection time for mapping measurements was typically 30 min.

Fourier Transform Infrared -Attenuated Total Reflectance Spectroscopy (FTIR-ATR)
We used FTIR-ATR to identify the chemical composition of the ground layer. Three samples (see Table 1) not embedded in resin were analyzed by placing their inner layer in direct contact with the ATR crystal. We used a spectrometer (PerkinElmer Frontier®) equipped with an ATR detector. Spectra were collected in transmittance mode, with 16 scans, in the wavenumber range from 4000 cm −1 to 650 cm −1 , at 1 cm −1 spectral resolution.

Micro-Raman Spectroscopy
We used micro-Raman Spectroscopy to identify the pigments in 16 samples, using a WiTec alpha 300R micro-Raman Spectrometer with a diode laser of 532 nm and operating power ranging from 0.06 mW to 0.18 mW. Sample irradiation diameter of the laser was approximately 1 µm. Spectra were measured from 0 cm −1 to 3000 cm −1 . Measurement conditions to provided a low signal/noise ratio were: 100x magnification, 50 cycles of accumulation and 0.5 s of integration time. Spectra were compared with reference pigments from CHSOS.

Analysis of the environmental and biological factors
MSI in VIS and UV, coupled with our computational tools, identified regions of possible biodeterioration. These regions were sampled for fungi and bacteria. Samples were collected using sterile cotton swabs from quadrants 54, 59 and 60 on Musas I and 50, 56, 59 and 69 on Musas II. They were transported in Phosphate Buffered Saline 75 and inoculated for culture in Potato Dextrose Agar (PDA) medium around 23 • C. Over the next three months, fungi were isolated and observed through optical microscopy at 100X in lactophenol cotton blue and Gram stain. In order to measure periodically environmental variables at the NTCR, six portable stations were developed with a lithium-ion polymer (LiPo) battery, and an Internet of Things (IoT) microcontroller device. The sensors used are the following: SHTC3 (Sensirion) to measure temperature and relative humidity, SGP30 (Adafruit) breakout board was used to monitor the variation of CO 2 concentration, and finally TSL2591 (Adafruit) breakout board was used to measure illuminance levels. Table 1. Paint stratigraphy samples. Pigment density, observed through optical microscopy, is categorized as l = low, m = medium or h = high, for each type of pigment: L = lead red, Vi = viridian, U = ultramarine, Ve = vermilion and C = chrome yellow. The average diameter of the crystals, in micrometers, is indicated in parenthesis next to the pigment symbols. Analysis symbology: OM = optical microscopy, R = Raman spectroscopy, EDX = energy dispersive X ray spectroscopy, UV = fluorescence microscopy and FTIR-ATR = Fourier transform infrared -attenuated total reflectance spectroscopy. For FTIR-ATR analysis, samples were not embedded. The rest of the analyses were carried out on cross-sections of samples.

Painting
Sample Name Visible colour This original painting tells the story of the study, describing our journey as we delve into the hidden secrets behind large-format artworks. A fluctuating and changing process, our multi-analytical study's use of novel software tools and multispectral imaging allowed us to fantasize, even for a second, that we could stop the passage of time and capture the artist's creative process, both materially and conceptually. As the colours flow through our fingers, we perceive individual pigment crystals, sense the damage caused by the passage of time, understand the materials and layers that come together to form the painting, and almost hear the artist's voice as we unveil his intentions. Although created using contemporary techniques such as acrylic and collage on canvas, this piece was inspired by Carlo Ferrario's creative process. His colour palette influenced our own, as we selected pigments found in Musas I and Musas II such as ultramarine blue, vermilion, and white. Our painting is structured similarly to Ferrario's, with two layers of ground, two layers of paint, and a final layer of varnish. Art, like history and time, slips inexorably through our fingers. Today, we capture in this study an instant of that flow, gaining new perspectives and revealing some of the hidden secrets behind large-format paintings. 14/18