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Article

Correlation of Visible Reflectance Spectrometry and Portable Raman Data for Red Pigment Identification

by
Anna M. Gueli
1,
Rosaria Galvagno
1,
Adriana Incardona
1,
Eleonora Pappalardo
2,
Giuseppe Politi
1,
Giuseppe Paladini
1,* and
Giuseppe Stella
1
1
Department of Physics and Astronomy “Ettore Majorana”, University of Catania, Via S. Sofia 64, 95123 Catania, Italy
2
Department of Educational Sciences, University of Catania, Via Teatro Greco 84, 95124 Catania, Italy
*
Author to whom correspondence should be addressed.
Heritage 2024, 7(4), 2161-2175; https://doi.org/10.3390/heritage7040102
Submission received: 5 January 2024 / Revised: 26 March 2024 / Accepted: 4 April 2024 / Published: 6 April 2024
(This article belongs to the Special Issue Pigment Identification of Cultural Heritage Materials)
Browse Figures
Versions Notes

Abstract

:
The accurate identification of pigments is of principal relevance in the field of cultural heritage conservation and restoration practices. In this preliminary study, a first attempt to set up a procedure for accurate red pigment identification, based on the assessment of the correlations existing between visible reflectance spectrometry (vis-RS) and Raman data, is presented. The proposed approach involved the realization of a library consisting of data acquired on a set of 35 pure red historical pigments supplied by ©Kremer Pigmente. In particular, vis-RS data, collected through a Konica Minolta CM-2600d spectrophotometer, were registered, together with the position of the Extrema Points (E.P.s) encompassing both the maximum and minimum points of the first derivative of the % spectral reflectance factor (SRF%) curves. Portable Raman spectroscopy measurements were collected by a B&W Tek Inc. portable Raman spectrometer equipped with a 785 nm laser. For each tested pigment, the positions and relative intensities of the characteristic Raman peaks were considered. The library was then tested for the characterization of the red/reddish painted areas of the medieval wall painting located within The Norman Castle of Aci Castello (Catania, Italy), and was shown to be essential for the unambiguous identification of the pigment used. It is worth noting that this study represents the first novel attempt to establish a reliable and efficient methodology for pigment identification, offering promising prospects in reducing uncertainties and ambiguities arising from the application of a single stand-alone approach.

1. Introduction

The accurate identification of pigments, through the employment of a combination of advanced analytical techniques and interdisciplinary collaborations, provides insights into the understanding of an artist’s creative process and helps in finding solutions to art historical research questions, including conservation, restoration, and authentication [1,2,3,4,5,6,7]. In fact, it enables restorers to authentically preserve the original intentions of artists, while enhancing our comprehension of historical techniques and cultural contexts. This process sheds light on the evolution of artistic practices and materials, tracing the development of pigments from ancient civilizations to modern times. Over the recent years, much interest has been notably directed towards the development of methodologies and novel strategies aimed at the comprehensive characterization of historical inorganic/organic pigmenting agents, alongside the identification of possible degradation products originating from the interaction of such agents with the surrounding environment [8,9,10,11,12,13,14,15,16]. These aspects play a fundamental role not only in estimating the choice and style of the artist but also in reproducing similar materials in accordance with ancient recipes which can be used for restoration interventions [17,18,19,20,21,22,23,24]. As is well known, the preservation and conservation of pigmented artworks generally require the employment of techniques that provide valuable information while minimizing the potential risk of damage. In this sense, a good strategy consists of the use of complementary non-invasive or, at least, micro-destructive methodologies able to perform in situ measurements with portable equipment that ensures the acquisition of useful data in conservation and restoration programs [25,26,27,28]. In this context, non-destructive techniques such as diffuse reflectance spectrometry, operating in the visible range (vis-RS) [29,30,31], and portable Raman spectroscopy [32,33,34,35,36,37] have proved to be essential and easily accessible for the assessment of various components in paintings [38]. In particular, since the beginning of the twenty-first century, vis-RS has become pivotal in the study of cultural heritage materials, i.e., in the ability to potentially discriminate both organic/inorganic materials [39,40], in the direct analysis of color discoloration on surfaces, as well as for the validation test of novel protective and cleaning consolidant products to be applied for conservation and preservation purposes. Notably, the reflection spectrum of a pigment has a behavior that strongly depends on the particle size, suspending medium, and refractive index of the investigated sample [41,42]. Nowadays, the ability to collect the reflectance contribution originating from small surface areas, in conjunction with easy-to-use, portable, and low-cost features, makes such methodology appealing in the characterization of artwork pure pigments [43]. Additionally, Raman spectroscopy has been largely utilized in the study of the molecular composition of pigment mixtures in paintings [13,44,45], ceramics [46,47,48], manuscripts [49,50], and wall paintings [51,52]. However, it is worth remarking that, in most cases, the presence of ground materials and binding agents, typically made up of organic-based compounds, is responsible for the onset of a high fluorescence background which may cover the weak Raman signal emitted by the pigment, making its identification a very challenging task. Currently, portable Raman spectrometers are commercially available. Despite the beneficial cost-effectiveness and user-friendly properties of portable instruments, which make them particularly appealing, their spectral resolution and signal-to-noise ratio cannot be compared with those of typical benchtop settings [53]. Nevertheless, the management of immovable objects of high artistic–historical value, i.e., frescoes and wall paintings, makes the employment of portable Raman instruments a fundamental requirement for their proper characterization [26,54,55,56,57] due to bureaucratic difficulties in relocating/transferring the artwork from the site to the laboratory. Whether using either vis-RS or portable Raman, the identification of the pigment “fingerprint” utilized in colored artworks may be challenging, especially for the characterization of objects affected by a poor preservation state. Accordingly, all measurement surveys should always be accompanied by a detailed comparison with a proper database for a feasible and unambiguous identification process. In this context, this paper mainly focuses on the description of an approach for accurate red pigment identification based on the association of the obtained data with those supplied by a home-made database containing vis-RS and portable Raman data of 35 pure red historical pigments provided by ©Kremer Pigmente (GmbH & Co. KG (Aichstetten, Germany)), achieved through the employment of portable instrumentations usable in situ. The aim was to establish a comprehensive repository capable of streamlining the red pigment identification process directly in the field, starting from a systematic comparison of the reference dataset with those achieved in a real context. This is extremely useful in all those cases in which unexpected challenges, such as instrumental issues, sample limitations, or ambiguous results, can impede a conclusive and reliable pigment recognition. In particular, the first step was devoted to the realization of a database consisting of integrated vis-RS and portable Raman data aimed at identifying potential “smart markers”, defined as a single, or an ensemble, of potential values/data characteristic of the sample which could serve as a strategical indicator for correlation analysis, thus enabling its insightful and unambiguous identification. The constructed database was then tested for the in situ multi-technique characterization of the red areas of a wall painting within The Norman Castle of Aci Castello (Catania, Italy), representing Mary with child, with the aim to validate and establish a reliable and efficient approach to pigment identification. Specifically, the state of preservation of the wall painting does not allow an easy reconstruction of the represented subject. No specific studies have been devoted to its analysis and no information about its existence inside the castle is available in the historical sources.
A further objective of this work is the improvement of the database of historical pigments available at the PH3DRA (PHysics for Dating Diagnostics Dosimetry Research and Applications) labs, in terms of vis-RS and portable Raman data, in agreement with the goals set in a previously published paper by some of the authors [58,59].

2. Materials and Methods

2.1. Materials

2.1.1. Red Pigments Used for Library

The set of pigments used for this study, composed of 35 powdered samples supplied in 3 mL containers, was purchased from ©Kremer Pigmente (Germany). The commercial names and reference numbers are specified in Table 1. The reference materials were studied without any further treatment. The examined set includes both pigments used in the past centuries, such as cinnabar (ID codes: 10620, 10621 and 10622), madder lake (ID code: 37202), and ochres (ID codes: 11574, 11575, 11576, 11577, 11584 and 11585), as well as modern and contemporary ones, i.e., alizarine (ID code: 23610) and carmine naccarat (ID code: 42100).

2.1.2. Wall Painting Located within the Norman Castle of Aci Castello (Catania, Italy)

Few traces of a wall painting survive inside a long rectangular room of the Castle, named “Byzantine Chapel”, but, chronologically consistent with the rest of the building, no traces of Byzantine architecture survive. As said above, no specific analysis was carried out on the wall painting, whose state of preservation makes the complete reading of the scene and the style particularly difficult. Traces of a seated subject survive, evidently Mary, bearing a small naked figure on her legs, Christ. Evanescent traces of a third figure, a Saint, are preserved to the right, while, on the left, the painting is too damaged. Mary wears a red dress and a black-bluish mantle. A light pink-yellowish color, at some points converting into white, is used for naked portions of the figures: the hand for Mary and the legs for Jesus. The scene is framed by a thin red rim.
A total of 10 different areas of the wall painting were analyzed in situ, representative of red finishing layers (R#, with # = 1, …, 10), as summarized in Table 2 and indicated in Figure 1.

2.2. Methods

2.2.1. Visible Reflectance Spectrometry (Vis-RS) Measurements

Vis-RS measurements were carried out by a Konica Minolta® model CM-2600d spectrophotometer (Konica Minolta, Tokyo, Japan), working in the 360–740 nm spectral range with a 10 nm acquisition step. The measurement geometry was d/8°, and the chosen measured area was 6 mm in diameter (SAV condition). The light provided by a Xenon lamp was diffused through an integrating sphere, uniformly illuminating the specimen. The light reflected by the sample then reached the optical system, where it was split into 10 nm-pitch components each processed by an analog processing circuit for the detection. In our case, a white calibration plate (CM-A145) and a black box (Zero-Calibration Box CM-A32 Minolta) were employed to perform the scale adjustment, as targets for the maximum and minimum lightness values [60]. The data elaboration, regarding SPEX/100 values (SPecular component EXcluded and UV included), was performed by dedicated software (SpectraMagic®, ver. 3.6), while the Origin® software (OriginPro 8) was used for data processing. The mean values for all data were obtained from three measurements, together with their respective uncertainties. The total uncertainty was then calculated using uncertainty propagation theory, which involves the square root of the sum of the squared standard deviation and the instrumental error. In particular, the instrumental error was evaluated considering the L*a*b* coordinates of the white calibration plate related to the 2° standard observer and D65 standard illuminant after the adjustment procedure.
The analysis involved first the behavior of the spectral reflectance factor (SRF%), accounting for the reflected/incident intensity percentage ratio, in the 400–700 nm range. Then, from the first derivative curves d(SRF%) of the spectral trends, the Extrema Points (E.P.s) related to the maximum, minimum, and inflection points were registered following the methodology proposed by Bacci et al. [39,61,62,63]. In particular, the high (absolute maxima, p-h) and low (relative maxima, p-l) peaks were recorded, together with the positions of valleys (absolute/relative minima, v) and shoulders (inflection points, sh).
For the evaluation of the derivative profiles, the Origin® software (OriginPro 8) was used, which performed, before the calculation of the first derivative, a smoothing on the two preceding and two following points of the discrete data related to the SRF% curves using a third-degree polynomial function.

2.2.2. Raman Measurements

Raman measurements were performed, both for the realization of the laboratory database and for the in situ analysis, through a portable modular setup consisting of a BTC162E Glacier® T (B&W TEK Inc., Newark, DE, USA) high-resolution double pass transmission-based spectrometer (BTC162E), an FC-D-785 laser module (CNI, Changchun, China), and a lab-grade probe. The instrument is characterized by an excitation wavelength of 785 nm (diode laser), a 350 mW maximum laser excitation power, and a response-enhanced linear CCD detector (thermoelectric cooled, TE). The laser output power was continuously regulated in order to maximize the signal-to-noise ratio while avoiding thermal effects on the sample. For our measurements, the laser power was maintained at between 3% and 10% of the maximum power, corresponding to ~10.5–35 mW, depending on the specific sample. The spot size was 85 μm in diameter at a working distance of 5.5 mm. Spectra have been registered in the 60–3150 cm−1 wavenumber range, by using different acquisition times and a nominal resolution of ~5 cm−1, adding several scans for each spectrum in order to improve the signal-to-noise ratio. Data elaboration, such as a baseline correction and a smoothing process, has been performed using the BWSpec 3.27 software.

3. Results and Discussion

3.1. Database Analysis Results

The proposed database involves the extraction of systematical information derived from the analysis of both the vis-RS and portable Raman data collected for each pigment within a limited range of wavelengths (i.e., visible) and using a single Raman excitation wavelength, respectively.
In Figure 2 we report, as an example, the Raman spectrum (Figure 2a) and the behavior of the SRF% (Figure 2b) together with the first derivative curve d(SRF%) vs. wavelength (Figure 2c) at SPEX/100 condition, obtained for sample ID code: 11300, red jasper, a glass-like transparency amorphous quartz, colored red by iron (III). This pigment was also rarely employed in historical painting layers [64] and in mosaics [65]. However, the selected sample was chosen mainly because it is particularly suitable for describing the database as it encompasses a wide range of different vis-RS and Raman features.
It is worth noting that the CM2600d instrument has a high degree of accuracy and precision. In terms of SRF%, the relative percentage error obtained was <1%, considering the instrumental error and the average of the three measurements. Thus, the true uncertainty is related to the position of the peak of the first derivative, which is ±10 nm.
The detected Raman profile (Figure 2a) revealed, as main features, intense peaks at 222 cm−1 (A1g), 287 cm−1 (Eg), 402 cm−1 (Eg), and 604 cm−1 (Eg), associated with ferric oxide (hematite, Fe2O3) [66], together with a contribution at 124 cm−1 and 462 cm−1 ascribable to quartz (SiO2) [66].
In particular, the general consistency between the observed compositions retrieved for all the investigated red pigments with those warranted in the datasheets provided by the pigment manufacturer was verified by comparing the experimental data with both the relevant scientific literature [67,68,69,70,71,72,73] and the accessible online databases, IRUG [74] and RRuff [75]. In our case, the collected Raman spectra (see Figure 3) have revealed distinct fingerprint features, whose positions are listed in Table 3 together with their relative intensities, in compliance with the certified composition of the investigated set.
In more detail, the portable Raman spectra of the three unique pigments (See Figure 3a) based on organic (23610—Alizarine crimson dark), As-(10800—Realgar), and Pb-based (42500—Red lead, minium) compounds (See Table 1 and Table 3) display the typical Raman signature of the components involved in the manufacture of the pigment, thus demonstrating uniform composition within the measurements. Red organic colors are typically challenging to analyze with the used set-up and excitation wavelength. Nevertheless, the portable Raman spectrum of 23610—Alizarine crimson dark (organic synthetic monoazo, naphthol) shows distinctive peaks at 1293 cm−1, 1322 cm−1, and 1479 cm−1, despite a strong fluorescence background and low signal-to-noise ratio, allowing for its proper identification. Concerning sample 10800—Realgar (As4S4), major features at 140 cm−1, 181 cm−1, 191 cm−1, 217 cm−1, 252 cm−1, 339 cm−1, and 351 cm−1 can be easily distinguished, corresponding to the vibrational modes of the arsenic–sulfur bonds present in the compound, while, in the case of 42500—Red lead, minium (Pb3O4), a portable-Raman profile with characteristic peaks at 118 cm−1, 149 cm−1, 222 cm−1, 312 cm−1 (lattice modes of the crystal structure) 389 cm−1, and 547 cm−1 (stretching and bending vibrations of the Pb-O bonds) was observed, in agreement with the literature.
Going on, the comparison of the portable Raman spectra related to natural (color index: PR102) iron oxide-based pigments (Figure 3b) revealed an almost uniform composition based on iron(III) oxide–hematite, recognized by its typical stretching bands corresponding to the Fe–O bond centered at 222 cm−1 and 494 cm−1, and deformation modes at 287 cm−1, 402 cm−1, and 604 cm−1. A mixture of hematite and gypsum (CaSO4·2H2O, main peak at 1008 cm−1) was identified in the case of sample 40510—Venetian red, as expected by the well-known and certified composition of this pigment, while, in the case of sample 40490—Satorius red and 40500—Red bolus, a mixture of hematite and minium (lead(II,IV) oxide) can be recognized, besides the main vibrational bands of iron(III) oxide, with a contribution at 143 cm−1. Finally, the portable Raman spectrum of the 11360—Brown-red slate sample only showed a distinguishable band at 287 cm−1 due to hematite, in addition to a series of broad bands falling within the 1130–1500 cm−1 range whose attribution remains doubtful.
Similarly, the portable Raman spectra collected on the synthetic (color index: PR101) iron oxide-based references (see Figure 3c) revealed, even in this case, an almost uniform composition based on iron(III) oxide–hematite according to the presence of the previously described modes. Some exceptions were recognized in the case of samples 40542 and 40545, respectively associated with light and deep English red, where a combination of hematite + gypsum and hematite + calcite was observed. Finally, with reference to the Hg-based pigments (11620—Natural Cinnabar, 11621—Natural Cinnabar, light, and 11622—Natural Cinnabar, powder), the portable Raman spectra (Figure 3d) revealed the three main contributions of vermilion, falling at 251 cm−1 (A1g), 280 cm−1 (Eg), and 344 cm−1 (Eg), in compliance with the certified datasheet provided by the manufacturer. Noteworthy, the employment of a red laser enables the acquisition of the highest-quality Raman spectrum in the case of vermilion due to a close matching of the laser frequency with an electronic transition of such compound. Consequently, resonant, or pre-resonant, Raman scattering phenomena are likely to occur, significantly amplifying the Raman signal by a factor ranging from 102 to 106.
However, in the case of Carmine naccarat (ID code: 42100), Dragon’s blood (ID code: 37000), Madder lake (ID code: 37202), Lac dye (ID code: 36020), and Resina kamala (ID code: 37030), the occurrence of a strong fluorescence background made the assessment of a reliable assignment difficult and, for this reason, their Raman signals will not be listed in the database. The same considerations can also be made for the pigment Burgundy red ochre deep 0–80 µ (ID code: 11576) and Burgundy red ochre deep 0–120 µ (ID code: 11577).
Concerning the vis-RS data, the SRF% curve reported in Figure 2b shows an increase for λ > 550 nm, typical of red-colored specimens, as expected. From a qualitative point of view, the behaviors of the SRF(%) profiles observed for the whole set were found to be slightly different from each other (See Figure 4), as it is known, they strongly depend on the specific properties of the pigment, including the particle size and crystal structure [76,77].
For pigments characterized by the same hue, the possibility to discern differences in their reflectance curves becomes challenging. In fact, the red pigments employed in this study show similar SRF(%) behavior characterized by the typical increase upon 550 nm < λ < 575 nm, with varying intensities. With the aim to provide further details, a first derivative calculation applied to the whole set of data was undertaken (see Supplementary Material (Figure S1)) for the assessment of the positions of the E.P.s characteristic of each red pigment. The set of E.P. positions for all the investigated red samples is listed in Table 3. An example is reported in Figure 2c, where the profile of d(SRF(%)) vs. λ is shown. The analysis of the d(SFR(%)) aids in identifying key wavelengths that contribute significantly to the hue of the pigment and mainly involves the identification of the most prominent maxima, accounting for the wavelength at which the pigment absorbs light mostly ( λ p − h ). In the case of Red jasper (sample ID code 11300) the λ p − h was found to be equal to 580 nm. A value of λ p − h = 580   ± 10   n m was also found for all the natural and synthetic iron oxide-based pigments, with the only exception being sample 52400—translucent red medium for which λ p − h = 600   n m . Higher values in the position of λ p − h were found for all the organic-based red pigments (23610—Alizarine crimson dark, 42100—carmine nacarat, 37000—Dragon’s blood, powder, 37202—Madder lake, 36020—Lac dye, and 37030—Resina kamala) ranging from 610 nm in the case of sample 42100—carmine nacarat to 640 nm for sample 37000—Dragon’s blood.
A more detailed analysis allowed for the identification of relative maxima ( λ p − l ), valleys (absolute/relative minima, λ v ), and shoulders ( λ s h ), described in Section 2.2.1, whose assessment provides refined insights into the spectral behavior of each investigated pigment. Notably, the identification of such features in the d(SRF(%)) turns out to be crucial in the validation phase, as we will discuss in the following, in order to formulate hypotheses on the pigment color and production technique. In our example, an inspection of Figure 2c revealed the occurrence of two valleys and two relative maxima, respectively located at 420 and 640 nm, and 440 nm and 500 nm, together with a slight shoulder, concave in shape, located at 680 nm [78].

3.2. On-Site Measurement Results

Vis-RS and portable Raman spectra were collected from the 10 different red spots shown in Figure 1 and Table 2 (see points R#). The comparison of the obtained data with those present in the laboratory database helped validate the model and establish a reliable and efficient methodology for red pigment identification.
In Figure 5 the obtained portable Raman spectra (Figure 5a), SRF% trends (Figure 5b), and calculated first derivate curves (Figure 5c) are reported, the latter obtained only for five representative red pigmented areas, lighter and darker, respectively.
By looking at the collected portable Raman spectra, the presence of calcite (CaCO3), from its main Raman contribution centered at 1086 cm−1, can be identified in R1, R4, R5, R8, and R10. The detection of calcium carbonate should be attributed to the inherent chemical composition of the background layer. The primary constituent of this layer is a calcite-based compound grounded in the Italian ‘fresco’ technique, in which pigments were applied directly on freshly laid mortar. However, the presence of calcite could be ascribed, other than to the background layer, to the possible use by the artist of a calcium carbonate-based white pigment, in a mixture with a pure red one, to obtain the desired lighter red hue observed by the naked eye in such areas (see Figure 1). Notably, in the case of the R2, R3, R6, R7, and R9 areas, no Raman signal at 1086 cm−1 can be distinguished.
Besides the presence of calcite, however, the high fluorescence background in the collected Raman spectra did not allow for the unambiguous identification of any other significant feature, except for a small signal around 285 cm−1 likely arising from the presence of iron oxides, making the identification of the red pigmenting a challenging task. In fact, from the database, a class of 14 different pigments display a Raman signature which can be comparable with those measured in situ. To overcome such limitation, a comparison of the d(SRF(%)) profiles, derived from the behavior of the corresponding spectral reflectance factor curves (Figure 5b,c), with those stored in the laboratory database allowed for the possibility to discriminate a set of 15 different red pigments with an absolute maximum in the d(SRF(%)) profile centered at 590 nm (p-h), i.e., pigment ID code: 48700, 48120, 40440, 40500, 40542, 40545, 11574, 11575, 11576, 11577, 48600, 48100, 48150, 48250, and 52350, well evident in the red areas of the wall painting.
To further refine the analysis, we compared the observed λ p − l , λ v , and λ s h features in the d(SRF(%)) of Figure 5c with those associated with the previously identified 15 red pigments recorded in the database (see Table 3). An inspection of Figure 5c reveals, besides the absolute maximum at 590 nm (λp-h), a relative minimum around 470 nm, and two relative maxima at 490 nm and 690 nm. The correlation of the Raman profiles, which exhibit a comparable signature with such features in the d(SRF(%)), together with the position of the absolute maximum, allowed us to discriminate the employment of red ochre as a red pigmenting agent based on similarity criteria, according to the scheme of Figure 6.
In Figure 7, we report the comparison of the d(SRF(%)) vs. λ between R1, R2, R4, R6, R7, and R9 and sample ID code 11574.
The presence of red ochre is supported by the fact that natural pigments were commonly used in wall painting from the Byzantine period to the Medieval one, sometimes associated with iron oxide.
It is worth noting that the aforementioned λ p − l and λ v features in the d(SRF(%)) curves represent more specific smart markers since they allow for the precise identification of a specific reference pigment which is otherwise not possible to uniquely accomplish from the absolute maximum at 590 nm ( λ p − l ) or from the small signal around 285 cm−1 observed in the Raman profile. This is useful not only for pigment recognition, allowing us to overcome a series of limitations related to the use of portable Raman spectroscopy as a single stand-alone approach, but also for making assumptions about the manufacturing technique. The fine-grained red ochre d(SRF(%)) profile (ID code: 11574) appears more compatible if compared, for example, to that of the coarse-grained red ochre (ID code: 11575).

4. Conclusions

In this work, we proposed a methodology for the accurate identification of red pigments used in historical–artistic contexts thanks to the evaluation of correlations that exist between vis-RS and portable Raman data, achieved through the employment of portable instrumentation. In particular, starting from the position of the Extrema Points (E.P.s) observed in the first derivative of the spectral reflectance factor (SRF%) curves and the Raman main vibrational signals collected for 35 pure red pigments supplied by ©Kremer Pigmente, a laboratory database was realized. The proposed database was then tested for the characterization of the red areas of a polychrome surface located within The Norman Castle of Aci Castello (Catania, Italy). As the main results, the comparison of the database data with those achieved in situ allowed us to discriminate, thanks to the identification of specific smart markers, the employment of fine-grained red ochre as a red pigmenting agent among a class of almost 15 different compatible pigments. In particular, it was shown that the identification of specific low-intensity features, i.e., relative maxima, relative minima, and shoulders in the d(SRF(%)) curves, which represent more specific smart markers, allowed us to validate the proposed procedure in cases in which ambiguous results can hinder a reliable pigment recognition.
Pigments of other hues will be analyzed and implemented in the database with the aim of verifying, in a more systematic manner, the proposed approach in different case studies.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/heritage7040102/s1, Figure S1: Profiles of the first derivative of spectral reflectance factor behavior of the 35 powdered samples reported in Table 1.

Author Contributions

Conceptualization, A.M.G., G.P. (Giuseppe Paladini) and G.S.; methodology, A.M.G., G.P. (Giuseppe Paladini) and G.S.; software, G.P. (Giuseppe Paladini) and G.S.; validation, E.P. and G.P. (Giuseppe Politi); formal analysis, G.P. (Giuseppe Paladini) and A.I.; investigation, R.G., G.P. (Giuseppe Paladini) and A.I.; resources, A.M.G. and G.S.; data curation, A.M.G., G.P. (Giuseppe Paladini) and G.S.; writing—original draft preparation, G.P. (Giuseppe Paladini), E.P. and G.S.; writing—review and editing, G.P. (Giuseppe Politi) and A.M.G.; visualization, A.M.G. and G.S.; supervision, A.M.G. and G.S.; project administration, G.S.; funding acquisition, A.M.G. All authors have read and agreed to the published version of the manuscript.

Funding

This research activity was funded by the European Union (NextGeneration EU), through the MUR-PNRR project SAMOTHRACE (ECS00000022).

Data Availability Statement

The data presented in this study are available on request from the corresponding author.

Acknowledgments

This research activity was funded by the European Union (NextGeneration EU), through the MUR-PNRR project SAMOTHRACE (ECS00000022) “SiciliAn MicronanOTecH Research And Innovation CEnter”–Ecosistema dell’innovazione (PNRR, Mission 4, Component 2 Investment 1.5, Avviso n. 3277 del 30-12-2021), Spoke 1–Università di Catania-Work Package 6 Cultural Heritage. The authors also wish to acknowledge the Aci Castello Municipality, the Ecomuseo Riviera dei Ciclopi, and, in particular, Major Carmelo Camillo Scandurra.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Investigated polychrome wall painting located within The Norman Castle of Aci Castello (Catania, Italy), with an indication of the analyzed areas. The yellow-dashed line indicates the painted area. The surface area is ~2 m2.
Figure 1. Investigated polychrome wall painting located within The Norman Castle of Aci Castello (Catania, Italy), with an indication of the analyzed areas. The yellow-dashed line indicates the painted area. The surface area is ~2 m2.
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Figure 2. (a) Portable Raman spectrum, (b) spectral reflectance factor (SRF%), and (c) d(SRF(%)) curve obtained for sample ID code 11300—Red jasper.
Figure 2. (a) Portable Raman spectrum, (b) spectral reflectance factor (SRF%), and (c) d(SRF(%)) curve obtained for sample ID code 11300—Red jasper.
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Figure 3. Measured portable Raman spectra of the 28 powdered samples reported in Table 3, using an excitation wavelength of 785 nm in the 100–1500 cm−1 spectral range. Spectra were vertically offset for clarity and divided into four different panels, respectively displaying measurements collected on: (a) three unique pigments based on organic (23610—Alizarine crimson dark), As-(10800—Realgar), and Pb-based (42500—Red lead, minium) compounds, (b) natural (color index: PR102) and (c) synthetic (color index: PR101) iron oxide-based pigments, and (d) Hg-based ones.
Figure 3. Measured portable Raman spectra of the 28 powdered samples reported in Table 3, using an excitation wavelength of 785 nm in the 100–1500 cm−1 spectral range. Spectra were vertically offset for clarity and divided into four different panels, respectively displaying measurements collected on: (a) three unique pigments based on organic (23610—Alizarine crimson dark), As-(10800—Realgar), and Pb-based (42500—Red lead, minium) compounds, (b) natural (color index: PR102) and (c) synthetic (color index: PR101) iron oxide-based pigments, and (d) Hg-based ones.
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Figure 4. Spectral reflectance factor (SRF%) behavior of the 35 powdered samples reported in Table 3. Spectra were sorted into four different panels, respectively representative of (a) eight unique pigments respectively based on organic (23610—Alizarine crimson dark, 42100—carmine nacarat, 37000—Dragon’s blood, powder, 37202—Madder lake, 36020—Lac dye, and 37030—Resina kamala), As-(10800—Realgar), and Pb-based compounds (42500—Red lead, minium), (b) natural (color index: PR102) and (c) synthetic (color index: PR101) iron oxide-based pigments, and (d) Hg-based ones.
Figure 4. Spectral reflectance factor (SRF%) behavior of the 35 powdered samples reported in Table 3. Spectra were sorted into four different panels, respectively representative of (a) eight unique pigments respectively based on organic (23610—Alizarine crimson dark, 42100—carmine nacarat, 37000—Dragon’s blood, powder, 37202—Madder lake, 36020—Lac dye, and 37030—Resina kamala), As-(10800—Realgar), and Pb-based compounds (42500—Red lead, minium), (b) natural (color index: PR102) and (c) synthetic (color index: PR101) iron oxide-based pigments, and (d) Hg-based ones.
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Figure 5. (a) Portable Raman spectra, (b) spectral reflectance factor (SRF%), and (c) calculated first derivate curves d(SRF(%)) (of five representative points for a feasible comparison) related to the red areas of the wall painting located at The Norman Castle of Aci Castello.
Figure 5. (a) Portable Raman spectra, (b) spectral reflectance factor (SRF%), and (c) calculated first derivate curves d(SRF(%)) (of five representative points for a feasible comparison) related to the red areas of the wall painting located at The Norman Castle of Aci Castello.
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Figure 6. Flowchart utilized for the red pigment identification. Blue and green arrows refer, respectively, to vis-RS and portable Raman data.
Figure 6. Flowchart utilized for the red pigment identification. Blue and green arrows refer, respectively, to vis-RS and portable Raman data.
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Figure 7. d(SRF(%)) vs. λ profiles related to the selected red areas and sample ID code 11574 (taken from the library), with the positions of the λ p − h , λ p − l , and λ v features indicated. All curves were normalized to the 590 nm absolute maximum value for a better comparison.
Figure 7. d(SRF(%)) vs. λ profiles related to the selected red areas and sample ID code 11574 (taken from the library), with the positions of the λ p − h , λ p − l , and λ v features indicated. All curves were normalized to the 590 nm absolute maximum value for a better comparison.
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Table 1. Identification code, color index, product name, and a brief description of the red pigments used in this paper (provided by the sales company).
Table 1. Identification code, color index, product name, and a brief description of the red pigments used in this paper (provided by the sales company).
ID CodeColor IndexProduct NameDescription
23610
42100
10800
42500
48700
11300
48120
40440
40351
40490
40500
40510
40542
40545
11574
11575
11576
11577
11584
11585
48600
11360
48100
48150
48200
48250
52350
52400
10620
10621		PR 83
NR 4.1, 75470
PY 39. 77085
PR 105 77578
PR 102
PW 27.77811
PR 101.77491
PBr 7
PR 102.77491
PR 102
PR 102
PR 102
PR 101
PR 101
PR 102
PR 101.77491
PR 102
PR 101.77491
PR 102
PR 102
PR101
PR102
PR 101.77491
PR 101.77491
PR 101.77491
PR 101.77491
PR 102. 77491
PR 101.77491
PR 106. 77766
PR 106. 77766		Alizarine crimson dark
Carmine naccarat
Realgar
Red lead, minium
Caput Mortuum reddish
Red jasper
Iron oxide red 120 m
Pompei red
Red mine ochre
Satorius red
Red bolus
Venetian red
English red light
English red deep
Burgundy red ochre medium, 0–80 µ
Burgundy red ochre medium 0–120 µ
Burgundy red ochre deep 0–80 µ
Burgundy red ochre deep 0–120 µ
Spanish red ochre 0–120 µ
Spanish red ochre 0–63 µ
Iron oxide red natural
Brown-red slate
Iron oxide red 110 m, light
Iron oxide red 130 b, medium
Iron oxide red 130 m, medium
Iron oxide red 222, dark
Translucent orange-red
Translucent red medium
Natural cinnabar
Natural cinnabar, light		Synthetic organic pigment
Natural aluminum lake of carminic acid
Natural orange-red sulphide of arsenic
Synthetic Pb-based pigment
Natural iron oxide pigment
Natural amorphous quartz
Synthetic iron oxide pigment
Synthetic iron oxide pigment
Natural iron oxide pigment
Natural iron oxide pigment
Natural iron oxide pigment
Natural iron oxide pigment
Synthetic iron oxide pigment
Synthetic iron oxide pigment
Natural iron oxide pigment
Synthetic iron oxide pigment
Natural iron oxide pigment
Synthetic iron oxide pigment
Natural iron oxide pigment
Natural iron oxide pigment
Synthetic iron oxide pigment
Natural iron oxide pigment
Synthetic iron oxide pigment
Synthetic iron oxide pigment
Synthetic iron oxide pigment
Synthetic iron oxide pigment
Natural iron oxide pigment
Synthetic iron oxide pigment
Natural HgS mineral mercuric sulfide
Natural HgS mineral mercuric sulfide
10622PR 106. 77766Natural cinnabar, powderNatural HgS mineral mercuric sulfide
37000
37202
36020
37030		NR 31
NR 9. 75330
NR 25.75450
NO 2.75310		Dragon’s blood, powder
Madder lake
Lac dye
Resina kamala		Natural organic pigment
Natural organic pigment
Natural organic pigment
Natural organic pigment
Table 2. Description of the investigated areas with the position within the wall painting.
Table 2. Description of the investigated areas with the position within the wall painting.
Analyzed AreaDescription Figure
R1Light-red area not assignable to any figure
R2Dark-red area not assignable to any figure
R3Dark-red areanot assignable to any figure
R4Red area not assignable to any figure
R5Red area halo of Mary
R6Red area not assignable to any figure
R7Red area not assignable to any figure
R8Light-red areanot assignable to any figure
R9Red area not assignable to any figure
R10Light-red area not assignable to any figure
Table 3. Main Raman signals (wavenumber and relative intensity) and E.P. positions extracted from the analysis of the d(SRF(%)) vs. λ profiles associated with the 35 powdered red pigments supplied by ©Kremer Pigmente.
Table 3. Main Raman signals (wavenumber and relative intensity) and E.P. positions extracted from the analysis of the d(SRF(%)) vs. λ profiles associated with the 35 powdered red pigments supplied by ©Kremer Pigmente.
ID CodeRaman Features 1 (cm−1)Extrema Points 2 (nm)
236101293 m, 1322 m, 1479 vs430 (v); 480 (sh); 510 (sh); 620 (p-h); 680 (v)
42100N/A410 (sh); 440 (v); 480 (sh); 520 (p-l); 550 (v); 610 (p-h); 680 (p-l)
10800140 m, 181 s, 191 s, 217 s, 252 m, 339 s, 351 vs420 (v); 480 (sh); 560 (p-h); 650 (p-l); 680 (p-l)
42500118 vs, 149 m, 222 w, 312 m, 389 m, 547 s570 (p-h); 650 (sh); 690 (p-l)
48700219 vs, 285 vs, 402 m, 483 w, 602 w420 (v); 460 (v); 510 (sh); 590 (p-h); 640 (v); 680 (sh)
11300120 s, 222 s, 287 vs, 402 s, 460 s, 604 s420 (v); 440 (p-l); 500 (p-l); 580 (p-h); 640 (v); 680 (sh)
48120219 vs, 287 vs, 402 m, 491 w, 604 w430 (v); 450 (p-l); 590 (p-h); 640 (v); 690 (sh)
40440287 w, 1194 br, 1282 br440 (p-l); 460 (v); 510 (sh); 590 (p-h); 650 (v); 680 (w); 690 (sh)
40351219 m, 287 s, 402 m, 604 w410 (v); 440 (v); 510 (sh); 580 (p-h); 640 (v); 680 (sh)
40490143 vs, 224 w, 290 m, 412 w, 605 w410 (v); 470 (v); 580 (p-h); 650 (v)
40500141 w, 290 w420 (v); 440 (p-l); 590 (p-h); 680 (p-l)
40510224 s, 289 vs, 411 s, 496 w, 609 w, 1008 m500 (sh); 580 (p-h); 640 (v); 690 (sh)
40542224 s, 290 vs, 1087 w420 (v); 440 (sh); 590 (p-h), 650 (v); 690 (sh)
40545224 s, 291 vs, 408 m, 496 s, 610 w, 1086 m420 (v); 440 (sh); 480 (p-l); 590 (p-h); 640 (v); 690 (p-l)
11574284 w470 (v); 490 (p-l); 590 (p-h); 660 (v); 680 (p-l)
11575284 w430 (p-l); 490 (p-l); 590 (p-h); 660 (p-l); 680 (p-l)
11576N/A500 (sh); 590 (p-h); 650 (v); 680 (sh)
11577N/A410 (v); 450 (p-l); 490 (p-l); 590 (p-h); 660 (v); 680 (sh)
11584224 w, 285 w410 (v); 450 (p-l); 580 (p-h); 650 (v); 680 (p-l)
11585224 w, 285 w440 (p-l); 490 (p-l); 580 (p-h); 680 (sh)
48600220 s, 291 vs, 408 m, 496 w, 610 w420 (v); 500 (sh); 590 (p-h); 640 (v); 650 (p-l)
11360284 w, 1130 w (br), 1228 s (br), 1288 s (br), 1428 s (br)420 (v); 460 (p-l); 480 (v); 500 (p-l); 510 (p-l); 570 (p-h); 640 (v)
48100219 s, 285 vs, 402 m, 493 w, 606 m430 (v); 460 (v); 500 (sh); 590 (p-h); 640 (v); 680 (sh)
48150222 vs, 287 vs, 404 m, 489 w, 606 m420 (sh); 450 (v); 510 (sh); 590 (p-h); 640 (v); 670 (sh)
48200222 vs, 287 vs, 404 m, 494 w, 606 m490 (p-l); 510 (sh); 580 (p-h); 640 (v); 670 (sh)
48250219 vs, 285 vs, 402 m, 485 w, 600 m450(v); 510 (sh); 590 (p-h); 650 (v); 680 (sh)
52350219 vs, 285 vs, 402 m, 484 w, 598 m440 (p-l); 590 (p-h); 640 (v); 690 (p-l)
52400219 w, 285 m, 400 m, 598 w480 (p-l); 510 (sh); 600 (p-h); 650 (v); 690 (sh)
10620250 vs, 280 m, 340 s430 (p-l); 460 (p-l); 510 (sh); 600 (p-h); 680 (sh)
10621252 vs, 280 m, 342 s420 (v); 600 (p-h)
10622252 vs, 283 m, 343 m420 (sh); 470 (sh); 500 (sh); 530 (sh); 610 (p-h); 680 (sh)
37000N/A420 (sh); 460 (v); 560 (sh); 640 (p-h); 680 (sh)
37202N/A440 (p-l); 480 (sh); 510 (p-l); 530 (v); 620 (p-h)
36020N/A470 (sh); 520 (sh); 610 (p-h)
37030N/A410 (sh); 440 (p-l); 470 (p-l); 510 (sh); 630 (p-h); 680 (p-h)
1 vs: very strong; s: strong; m: medium; w: weak; sh: shoulder; br: broad. 2 p-h: high peak; p-l: low peak; v: valley; sh: shoulder.
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MDPI and ACS Style

Gueli, A.M.; Galvagno, R.; Incardona, A.; Pappalardo, E.; Politi, G.; Paladini, G.; Stella, G. Correlation of Visible Reflectance Spectrometry and Portable Raman Data for Red Pigment Identification. Heritage 2024, 7, 2161-2175. https://doi.org/10.3390/heritage7040102

AMA Style

Gueli AM, Galvagno R, Incardona A, Pappalardo E, Politi G, Paladini G, Stella G. Correlation of Visible Reflectance Spectrometry and Portable Raman Data for Red Pigment Identification. Heritage. 2024; 7(4):2161-2175. https://doi.org/10.3390/heritage7040102

Chicago/Turabian Style

Gueli, Anna M., Rosaria Galvagno, Adriana Incardona, Eleonora Pappalardo, Giuseppe Politi, Giuseppe Paladini, and Giuseppe Stella. 2024. "Correlation of Visible Reflectance Spectrometry and Portable Raman Data for Red Pigment Identification" Heritage 7, no. 4: 2161-2175. https://doi.org/10.3390/heritage7040102

APA Style

Gueli, A. M., Galvagno, R., Incardona, A., Pappalardo, E., Politi, G., Paladini, G., & Stella, G. (2024). Correlation of Visible Reflectance Spectrometry and Portable Raman Data for Red Pigment Identification. Heritage, 7(4), 2161-2175. https://doi.org/10.3390/heritage7040102

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