# Developing a non-invasive approach for prehistoric rock art characterization based on smartphone imaging colorimetry and spectroscopy

Mirco Ramacciotti, Gianni Gallello, Esther López-Montalvo, Sonia Rubio-Barberá, Lidia Herreros Chavez, M. Luisa Cervera, Ángel Morales-Rubio, Sonia Murcia Mascarós, Clodoaldo Roldán-García

<figure><img src="image_1.png" /></figure>

## ► To cite this version:

Mirco Ramacciotti, Gianni Gallello, Esther López-Montalvo, Sonia Rubio-Barberá, Lidia Herreros
Chavez, et al.. Developing a non-invasive approach for prehistoric rock art characterization based on
smartphone imaging colorimetry and spectroscopy. Journal of Cultural Heritage, 2025, 76, pp.338-349.
10.1016/j.culher.2025.10.019 . hal-05358009

HAL Id: hal-05358009

https://hal.science/hal-05358009v1

Submitted on 10 Nov 2025

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_2.png" /></figure>
<figure><img src="image_3.png" /></figure>

Original article

# Developing a non-invasive approach for prehistoric rock art characterization based on smartphone imaging colorimetry and spectroscopy

<figure><img src="image_4.png" /></figure>

Mirco Ramacciotti<sup>a,b,*</sup>, Gianni Gallello<sup>a,c</sup>, Esther López-Montalvo<sup>d</sup>, Sonia Rubio-Barberá<sup>e</sup>, Lidia Herreros Chavez<sup>e</sup>, M. Luisa Cervera<sup>e</sup>, Ángel Morales-Rubio<sup>e</sup>, Sonia Murcia Mascarós<sup>c</sup>, Clodoaldo Roldán-García<sup>c</sup>

## ARTICLE INFO

**Article history:**

Received 1 October 2025

Accepted 25 October 2025

**Keywords:**

Smartphone

Image analysis

Spanish Levantine Rock Art

Pigment

Binder

Experimental archaeology

## ABSTRACT

The development of non-invasive archaeometric methods for the in-situ characterization of rock art panels is crucial for the archaeological study and the conservation of these unique remains.

This study aims to test a non-invasive analytical approach based on smartphone imaging colorimetry and portable spectrometers for rock art characterization on experimental replicas made on slabs of carbonate rocks by employing iron (III) oxide as pigment and water, milk and marrow as binders.

The painted areas and the supports were firstly characterized from the colorimetric point of view using a spectroradiometer and by smartphone imaging colorimetry to assess the potentials of this innovative technique. Furthermore, optical microscopy, and atomic and molecular spectroscopy were employed.

A first methodological on-field test of imaging colorimetry was also carried out at the Levantine rock art site of Coco de la Gralla (Mas de Barberans, Spain), where smartphone data were cross-referenced to those from portable X-ray fluorescence.

Data suggest that smartphone imaging could be a useful technique for a first colorimetric characterization of rock art, though several factors should be considered in the interpretation of the results, including not only painting materials, but also rock support characteristics and the variability of the conservation within the site. Furthermore, all the techniques revealed their potentials for the chemical and colorimetric characterization of the pictorial layer, while the limitations are caused by instrumental sensitivity, and by the influence of support features.

© 2025 The Author(s). Published by Elsevier Masson SAS. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/)

## 1. Introduction and research aim

Archaeometric methods have a crucial role for the study of rock art due to their capability of identifying the type of materials employed for their production, including the pigments from organic (charcoal) or mineral sources, and the binding materials, contribut-

ing to the comprehension of the technical processes beyond their creation [1–3], as well as to evaluate their state of conservation and the potential decay phenomena [4,5]. In fact, throughout the centuries and the millennia, rock substrates are subjected to anthropic, biological, chemical and environmental factors which can contribute to the development of crusts and efflorescence made up of oxalates, sulphates and other secondary phases [6–9], and to alterations of the physical properties of the rock support through exfoliation, flaking, honeycombing, etc. [10,11], as well as to the deterioration and degradation of the pictorial materials [12,13].

<footnote><sup>a</sup> Department of Prehistory, Faculty of Geography and History, PREMEDOC Research Group, Archaeology and Ancient History, University of Valencia, Valencia, Spain</footnote>

<footnote><sup>b</sup> University Bordeaux Montaigne, Archéosciences Bordeaux (UMR 6034), Pessac, France</footnote>

<footnote><sup>c</sup> Institute of Materials Science, University of Valencia, Valencia, Spain</footnote>

<footnote><sup>d</sup> French National Center for the Scientific Research (CNRS)-University of Toulouse 2-Jean Jaurès, UMR 5608 TRACES, Toulouse, France</footnote>

<footnote><sup>e</sup> Department of Analytical Chemistry, Faculty of Chemistry, University of Valencia, Valencia, Spain</footnote>

<footnote>* Corresponding author.</footnote>

<footnote>E-mail addresses: mirco.ramacciotti@uv.es (M. Ramacciotti), gianni.gallello@uv.es (G. Gallello), esther.lopez-montalvo@cnrs.fr (E. López-Montalvo), m.luisa.cervera@uv.es (M.L. Cervera), angel.morales@uv.es (Á. Morales-Rubio), sonia.mascaros@uv.es (S.M. Mascarós), clodoaldo.roldan@uv.es (C. Roldán-García).</footnote>
Due to the fragility and uniqueness of these panels, the de-
velopment of non-invasive analytical protocols is of pivotal impor-
tance [14].

The examination of color variations is often the starting point
in order to study the materials and the conservation state of a
panel. Color can indeed be a primary marker of the variability in
raw material properties and processing, as well as of other techni-
cal aspects related to painting [15–20]. Furthermore, color alter-
ations can be produced by weathering phenomena [8,13,21–23].
This makes color features particularly important to evaluate and
monitor the state of conservation of rock art panels, which is also
a preliminary step for any following analytical approach. Standard
color charts, spectrocolorimeters or spectroradiometers are often
used to characterize color at rock art sites [19,24–26]. The main
advantage of these techniques relies in their capability of providing
easily communicable and reproducible data which are, at different
extent [27,28], independent from researcher subjectivity. Efforts
have been also made in order to improve digital images for the
appropriate recording, reproduction and communication of color in
rock art panels [24,29,30], as well as for visualization enhancement
of the motifs [31–33].

In the last few years, portable spectrometers have been more
and more used in rock art study. Raman and Fourier transform
infrared spectroscopy (FT-IR) are employed to identify the type
of pigment and detect traces of organic compounds potentially
linked to binding materials, while portable energy dispersive
X-ray fluorescence (pEDXRF) is a widely used technique to deter-
mine the elemental composition of pictorial layer and support [34–
37]. However, to offer a better characterization of the materials
or dating them, sometimes invasive analyses must be carried out
[1,38,39]. In this case, a first analytical survey is fundamental to
choose the appropriate sampling areas and minimize the impact
of the procedure [2]. The interpretation of the analytical results
is often challenging since they come from the interaction of the
signals from the rock-wall, from the pictorial layers and from the
possible superimposed strata produced by weathering phenomena
[35–38,40–42].

In order to push forward the development of a non-invasive an-
alytical approach, we made laboratory replicas simulating rock art
painting based on information obtained from previous studies and
ethnographic research [1,14,39,43-46]. Three carbonate rock slabs
were painted by employing different quantities of red ochre, and
organic and non-organic binders. Then, the different supports and
the painted areas were characterized. Smartphone imaging col-
orimetry has been used to characterize the samples, together with
standardized techniques like optical microscopy, Raman, Fourier
transform near infrared spectroscopy (FT-NIR) and pEDXRF. Smart-
phone imaging colorimetry allows to rapidly run analyses with
a widespread, economic and easily portable device and it has
been recently implemented in archaeometry to characterize mor-
tars [47], siliceous artefacts [48], frescoes and coins [49-51]. Con-
cerning rock art, Ruiz and Pereira tested a commercial mobile app
for the colorimetric characterization of Marmalo III rock art site
(Villar del Humo, Cuenca, Spain) obtaining unreliable data [24].
On the contrary, the pilot exploratory analysis of our approach
has been promising [52], though its possible role in an analyti-
cal protocol is far from being fully explored. The application of
these techniques in a controlled environment and on experimen-
tal replicas allowed to better understand the influence of support
and pictorial layer colorimetric and chemical features on the ana-
lytical results, permitting to investigate their limitations and po-
tentials to enhance the use of non-invasive techniques, employ-
ing smartphone imaging colorimetry as a first screening supple-
mentary tool. Finally, a pilot fieldwork test of smartphone imaging
colorimetry was carried out at Coco de la Gralla (Mas de Barber-
ans, Spain) Levantine rock art site, where pEDXRF analyses were

previously performed by some of the authors of the present work
[40].

## 2. Materials and methods

### 2.1. *The samples*

#### 2.1.1. *Experimental replicas*

Three carbonate rock slabs were used to make the experimen-
tal replicas, being carbonate rock among the most common litholo-
gies at Levantine rock art sites [14]. The slabs were worked to cre-
ate separated and smooth rows. This operation eased the painting
procedure. Furthermore, the production of virtually equal support
surface removed roughness and irregularity as variables, allowing
to better understand the role of support colorimetric and chemi-
cal features on the results. The samples (Fig 1) consist of twenty-
seven squares (side: ~2 cm) painted on the supports with increas-
ing amounts of a Fe₂O₃ red pigment, from ~0.2 g (Point I) up to
~1.2 g (Point VI). Ten drops of three different binders were used:
ultra-pure water (W), cow bone marrow (MA) heated to obtain
a liquid mixture, and commercial cow milk (M). Blanks (Point O)
were made with only the binders: MA21 for marrow and M22 for
milk, while the bare support (LS2) was considered the blank of wa-
ter samples. Three samples (Replicate) were made for each binder
types with the same amount of pigment (~0.7 g). The supports
(LS1, LS2 and LS3) were also analyzed.

Red pigment powder was put in a watch glass, and the binder
was poured with a dropper. Pigment and binder were mixed with
a stick. The homogenized mixture was applied on the area with a
brush made with animal hairs up to its depletion.

Table 1 show samples and their main features.

The slabs show different colors, being LS2 lighter than LS1 and
LS3. Blank samples MA21 and M22 have light greyish patched sur-
faces. Colored samples are red, and color surface appear darker
and more homogeneous in samples where more pigment was em-
ployed.

### 2.1.2. *Coco de la Gralla rock art site*

A first test was carried out on some figures from Coco de la
Gralla rock-shelter, a Levantine rock art site located in the coun-
tryside of Mas de Barberans (Tarragona, Spain).

<figure><img src="image_5.png" /><figcaption>Fig. 1. The painted supports LS1, LS2 and LS3.</figcaption></figure>
<table><caption>**Table 1**

Analyzed samples and their main features.</caption><thead><tr><th>Sample</th><th>Point</th><th>Binder</th><th>Fe<sub>2</sub>O<sub>3</sub> (g)</th><th>Support</th><th>Sample</th><th>Point</th><th>Binder</th><th>Fe<sub>2</sub>O<sub>3</sub> (g)</th><th>Support</th></tr></thead><tbody><tr><td>M22</td><td>Point 0</td><td>Milk</td><td>0</td><td>LS1</td><td>MA20</td><td>Point VI</td><td>Marrow</td><td>0.1232</td><td>LS1</td></tr><tr><td>M23</td><td>Point I</td><td>Milk</td><td>0.0195</td><td>LS1</td><td>MA43</td><td>Replicate</td><td>Marrow</td><td>0.0744</td><td>LS2</td></tr><tr><td>M25</td><td>Point II</td><td>Milk</td><td>0.0368</td><td>LS1</td><td>MA44</td><td>Replicate</td><td>Marrow</td><td>0.0744</td><td>LS2</td></tr><tr><td>M26</td><td>Point III</td><td>Milk</td><td>0.0558</td><td>LS1</td><td>MA45</td><td>Replicate</td><td>Marrow</td><td>0.0744</td><td>LS2</td></tr><tr><td>M27</td><td>Point IV</td><td>Milk</td><td>0.0765</td><td>LS1</td><td>W36</td><td>Point I</td><td>Water</td><td>0.0195</td><td>LS2</td></tr><tr><td>M28</td><td>Point V</td><td>Milk</td><td>0.0999</td><td>LS1</td><td>W37</td><td>Point II</td><td>Water</td><td>0.0499</td><td>LS2</td></tr><tr><td>M29</td><td>Point VI</td><td>Milk</td><td>0.1204</td><td>LS1</td><td>W38</td><td>Point III</td><td>Water</td><td>0.0725</td><td>LS2</td></tr><tr><td>M09</td><td>Replicate</td><td>Milk</td><td>0.0713</td><td>LS3</td><td>W39</td><td>Point IV</td><td>Water</td><td>0.0918</td><td>LS2</td></tr><tr><td>M10</td><td>Replicate</td><td>Milk</td><td>0.0713</td><td>LS3</td><td>W41</td><td>Point V</td><td>Water</td><td>0.102</td><td>LS2</td></tr><tr><td>M11</td><td>Replicate</td><td>Milk</td><td>0.0713</td><td>LS3</td><td>W42</td><td>Point VI</td><td>Water</td><td>0.1202</td><td>LS2</td></tr><tr><td>MA21</td><td>Point 0</td><td>Marrow</td><td>0</td><td>LS1</td><td>W12</td><td>Replicate</td><td>Water</td><td>0.0682</td><td>LS3</td></tr><tr><td>MA15</td><td>Point I</td><td>Marrow</td><td>0.0203</td><td>LS1</td><td>W13</td><td>Replicate</td><td>Water</td><td>0.0682</td><td>LS3</td></tr><tr><td>MA16</td><td>Point II</td><td>Marrow</td><td>0.0403</td><td>LS1</td><td>W14</td><td>Replicate</td><td>Water</td><td>0.0682</td><td>LS3</td></tr><tr><td>MA17</td><td>Point III</td><td>Marrow</td><td>0.0616</td><td>LS1</td><td>LS1</td><td>-</td><td>Support</td><td>0</td><td>-</td></tr><tr><td>MA18</td><td>Point IV</td><td>Marrow</td><td>0.0784</td><td>LS1</td><td>LS2</td><td>Point 0<sup>a</sup></td><td>Support</td><td>0</td><td>-</td></tr><tr><td>MA19</td><td>Point V</td><td>Marrow</td><td>0.1078</td><td>LS1</td><td>LS3</td><td>-</td><td>Support</td><td>0</td><td>-</td></tr></tbody></table>

Note:

a Point LS2 was considered Point 0 for water samples.

<table><caption>**Table 2**

Analyzed points of Coco de la Gralla.</caption><thead><tr><th>Point</th><th>Class</th><th>Point</th><th>Class</th></tr></thead><tbody><tr><td>1.P1</td><td>Animal</td><td>14.P1</td><td>Classic pachypod</td></tr><tr><td>1.B1</td><td>Rock support (bare wall)</td><td>14.P2</td><td>Classic pachypod</td></tr><tr><td>2.P1</td><td>Stylized pachypod</td><td>14.B1</td><td>Rock support (bare wall)</td></tr><tr><td>2.B1</td><td>Rock support (bare wall)</td><td>15.P1</td><td>Stylized pachypod</td></tr><tr><td>3.P1</td><td>Indeterminate</td><td>15.B1</td><td>Rock support (bare wall)</td></tr><tr><td>4.P1</td><td>Indeterminate</td><td>16.P1</td><td>Stylized pachypod</td></tr><tr><td>5.P1</td><td>Indeterminate</td><td>16.P2</td><td>Stylized pachypod</td></tr><tr><td>5.B1</td><td>Rock support (bare wall)</td><td>16.B1</td><td>Rock support (bare wall)</td></tr><tr><td>6.P1</td><td>Animal</td><td>17.P1</td><td>Classic pachypod</td></tr><tr><td>6.B1</td><td>Rock support (bare wall)</td><td>17.P2</td><td>Classic pachypod</td></tr><tr><td>7.P1</td><td>Classic pachypod</td><td>17.C1</td><td>Rock wall (calcite coating)</td></tr><tr><td>7.P2</td><td>Classic pachypod</td><td>17.F2</td><td>Rock support (fresh rock)</td></tr><tr><td>7.B1</td><td>Rock support (bare wall)</td><td>18.P1</td><td>Classic pachypod</td></tr><tr><td>8.P1</td><td>Compact pachypod</td><td>18.P2</td><td>Classic pachypod</td></tr><tr><td>8.F1</td><td>Rock support (fresh rock)</td><td>18.P3</td><td>Classic pachypod</td></tr><tr><td>9.P1</td><td>Classic pachypod</td><td>18.B1</td><td>Rock support (bare wall)</td></tr><tr><td>9.B1</td><td>Rock support (bare wall)</td><td>18.B2</td><td>Rock support (bare wall)</td></tr><tr><td>9.P2</td><td>Classic pachypod</td><td>19.P1</td><td>Indeterminate</td></tr><tr><td>10.P1</td><td>Compact pachypod</td><td>19.F1</td><td>Rock support (fresh rock)</td></tr><tr><td>11.P1</td><td>Classic-stylized pachypod</td><td>22.P1</td><td>Compact pachypod</td></tr><tr><td>11.B1</td><td>Rock wall (bare wall)</td><td>22.B1</td><td>Rock wall (bare wall)</td></tr><tr><td>12.P1</td><td>Stylized pachypod</td><td></td><td></td></tr><tr><td>13.P1</td><td>Stylized pachypod</td><td></td><td></td></tr><tr><td>13.B1</td><td>Rock wall (bare wall)</td><td></td><td></td></tr></tbody></table>

Twenty figures (1–19, 22) were considered, and the painted (P; twenty-seven points) and surrounding rock (B, F, C; eighteen points) surfaces were characterized (Table 2).

The characterized regions of interest (ROI), showed in the Supplementary Materials (SM1), correspond to the spots analyzed by pEDXRF [40]. Three classes of surface were identified: bare wall (undecorated surface altered by weathering phenomena), fresh rock (spots chipped after paint making) and calcite coating (deposit of calcite from the upper areas of the rock wall).

Details on Coco de la Gralla panel and the pEDXRF study can be found in a recent paper by López-Montalvo and colleagues [40].

## 2.2. Colorimetry

Color analysis was performed employing two techniques.

Samples and reference white were photographed in a lightbox (camera-sample distance ~ 35 cm; Fig. 2) employing a Samsung

Galaxy S7 Edge (ISO: 80, f: 1/500 s, T: 3700 K, focus: manual, zoom: x 5.0). Photos from supports LS1, LS2 and LS3 were made. Images were processed with MATLAB R2025a (MathWorks) and COLORLAB toolbox [53]. Firstly, each picture was cut to process only ROI data (Fig 2), RGB parameters from each pixel were converted to XYZ tristimulus values and, finally, to the coordinates of the CIELAB (CIE L*a*b*) color space employing reference white XYZ data [54]. These operations are carried out employing the in-built MATLAB functions rgb2xyz() and xyz2lab().

Samples and reference white were also analyzed using a SpectraScan® PR 655 Photo Research® spectroradiometer. Radiance was measured in the visible region (380–780 nm), then, coordinates in CIELAB color space were calculated from the tristimulus values (XYZ) employing the reference white.

Polynomial regression models of degrees 1 to 3 were built employing the colored samples as training set, and RGB data from smartphone pictures and CIELAB coordinates obtained by spectroradiometer to characterize smartphone camera. To study the feasibility of in-lab made standards for camera calibration suitable for prehistoric paintings, CIELAB coordinates were predicted based on RGB values of the same points according to the models. Furthermore, leave-one-out cross-validation (LOOCV) was employed to evaluate their robustness. Predicted CIELAB coordinates were compared to those from spectroradiometer according to CIE 2000 Color-Difference Formula [55] and considering a threshold distance (ΔE₀₀) of 3.5, which is the proposed tolerance limit for printed materials [56], as suggested also by Sáez-Hernández et al. [51]. Distances in lightness (ΔL*), chroma (ΔC*), being C* = $\sqrt{a^*^2 + b^*^2}$ and hue (ΔH*), being hue h* = $\tan^{-1}\frac{b^*}{a^*}$) were also calculated according to CIE 2000 [55] to observe biases.

The photos at Coco de la Gralla were taken under daylight conditions during the fieldwork campaign of pEDXRF analyses [40] by using the above-quoted smartphone in automatic mode. The photos were processed by MATLAB. The ROIs were selected manually and correspond to the areas previously analyzed with pEDXRF by López-Montalvo and colleagues [40]. The lightest achromatic color target of a QP Color Reference Card (QP Card 203) was used in each photo for the conversion of the RGB values of the ROIs to the CIELAB color space.

## 2.3. Optical microscopy

The surface of painted samples employing different binders were compared by microscopic images employing a conventional optical microscope (magnification: x60 at 500 µm scale).
<figure><img src="image_6.png" /><figcaption>**Fig. 2.** Experimental conditions for smartphone photos (left), picture (center) and ROI (right) of M29.</figcaption></figure>

## 2.4. Molecular spectroscopy

A portable i-Raman® Plus spectrometer (BWS465-785S) by BandW Tek was employed analysis. The samples were measured using a lab-grade fiber optic probe (BAC102-785-5M). Laser wavelength was 785 nm with a power of 49.5 mW to 148.5 mW and a measurement spot of 80 µm. Measurements were performed in the 3350-150 cm⁻¹ region with an integration time of 1 to 3 s and three replicates (resolution < 4.5 cm⁻¹). Each spectrum is the mean of two up to five measurements in different spots.

A MPA model Multipurpose Analyzer Fourier Transform near-infrared spectrometer (FT-NIR) by Bruker was employed, equipped with an integrating sphere and a fiber optic probe used for diffuse reflectance spectra acquisition. Samples and supports were measured three times with an optical fiber probe to obtain FT-NIR spectra by diffuse reflectance. Spectra were recorded in Kubelka-Munk units in the 14,000-4000 cm⁻¹ region, using a resolution of 4 cm⁻¹ and cumulating 50 scans. Background spectrum was acquired from the closed integrating sphere under same conditions.

## 2.5. Multielement analysis

The analyses were carried out with a S1 Titan pEDXRF by Bruker equipped with a rhodium X-ray tube (50 kV) and X-Flash® SDD (silicon drift detector; energy resolution: FWHM = 147 eV at 5.9 keV). Geochem-trace internal calibration was used for quantification (Al, Si, K, Ca, Ti, Cr, Mn, Fe, Ni, Cu, Zn, Rb, Sr, Zr). Each sample was measured in three points.

## 2.6. Data analysis

Data analysis and visualization was carried out with R [57] and ggplot2 package [58]. Principal component analysis (PCA) and cluster analysis (method: average linkage) were employed for data exploration. Standardized L\*, a\* and b\* coordinates of painted points, blanks and supports were used as variables. Also, PCA was employed to explore multielement analysis data. Elemental concentrations (Al, Si, S, Ca, Ti, Fe, Cu, Zn, Sr) were transformed by centered log-ratio (clr) followed by standardization [59]. Finally, PCA was used to explore colorimetric data obtained at Coco de la Gralla together with the previously published elemental concentrations obtained by pEDXRF (Al, Si, Ca, Ti, Fe, Cu, Sr, Zn) [40]. Concentrations were transformed by clr; then, data were standardized.

# 3. Results and discussion

## 3.1. Optical microscopy

Photos of painted surfaces of the lowest and the highest points made with the three binders are shown in the Supplementary Materials (SM2).

W36, M23 and MA15 correspond to the samples made with the lowest amounts of pigment, while W42, M29 and MA20 to those made with the highest amounts. More surface is covered by the pigment in the samples with the highest amount of painting materials than in the others. However, MA20 seems to have less covered surface than M29 and W42.

## 3.2. Molecular and atomic spectroscopy

### 3.2.1. Raman spectroscopy

Spectra obtained by portable Raman spectroscopy are shown in the Supplementary Materials (SM3).

Characteristic bands of synthetic iron (III) oxide are observable (~224, ~291, ~407 and ~610 cm⁻¹) [60] in some samples made with milk (M25-28) and water (W12-14, W37-39, W41-42). Furthermore, LS2 and all its samples painted with both marrow (MA43-45) and water (W36-42) show a characteristic band of calcite at ~1086 cm⁻¹ [61]. On the other hand, features of LS1 and LS3 could not be obtained due to fluorescence. Samples painted with marrow on LS1 do not show any evident peak, probably due to matrix effect and the limited setting options for the spectrometer caused by fluorescence, possibly worsened by the presence of organic residues from this binder. Bands attributable to organic compounds could not be recognized.

### 3.2.2. FT-NIR spectroscopy

Whole raw spectra are reported in the Supplementary Materials (SM4).

The main features of the spectra in the region between 14,000 and 8000 cm⁻¹ is a broad band between 13,000 and 10,000 cm⁻¹ (Supplementary Materials, SM4a). Absorption features for iron oxides and hydroxides appear in this region [62]. Indeed, it can be appreciated in most of the spectra of painted samples while it is absent in the support (LS1-3) and in the blank ones (M22, MA21). Sample MA21 shows another small band in this region peaking at ~8230 cm⁻¹, probably linked to C-H stretching vibration 2nd overtones of CH₂ and CH₃, associated with the presence of lipids [63,64].

Features were found in the region between 8000 and 4000 cm⁻¹ (Fig. 3). Most of the bands can be appreciated both in the samples and in the supports (bare rock spectra are shown in the Supplementary Materials, SM4b) and should be attributed to the compounds present in the rocks. However, the three slabs show different features. The band at t ~7100 cm⁻¹ can be attributed to the first overtone of OH stretch; furthermore, the features of this band in LS3 suggest the presence of clay minerals, corroborated by the other doublet absorption band peaking at ~4630 and ~4530 cm⁻¹, ascribable to metal-OH bend and OH stretch combinations [65]. The bands between ~5330 and ~5200 cm⁻¹ probably indicate the presence of water [65]. The features at
<figure><img src="image_7.png" /><figcaption>Fig. 3. FT-NIR spectra of the samples in the region between 8000 and 4000 cm⁻¹. Spectra were offset to avoid overlapping.</figcaption></figure>

~5000, ~4630 and ~4300 cm⁻¹ can be combination and overtone bands of the CO₃ fundamentals [65]. The position of peaks in the ~4300 cm⁻¹ band of LS1 compared to LS2-3 could be determined by the different proportions in carbonate minerals in the supports since different carbonates peak at slightly different wavenumbers: calcite has a band at ~4280 cm⁻¹, while dolomite and magnesite at ~4310 cm⁻¹ and at ~4350 cm⁻¹ respectively [66]. This is consistent with the higher levels of Ca in LS2-3 than in LS1. The region of the spectra up to 4000 cm⁻¹ probably shows a part of the carbonate band which peaks at ~4000–3900 cm⁻¹, depending on the minerals [65], and hydroxyl group vibrational modes [67].

Concerning the binders, the bands at ~5780 cm⁻¹ and the bands at ~4325 and ~4250 cm⁻¹ are probably related to C–H vibration modes and can be connected to the presence of lipids [64,68]. The former ones appear in all the marrow samples and in M22 while the latter only in marrow samples. It is worth noting that higher amounts of pigment seem to mask the spectral features of the binders, as suggested also by the absence of lipid bands at ~8200 cm⁻¹, visible only in marrow blank (MA21; Supplementary Materials, SM4a). In fact, bands from organic matter are evident only in blank milk spectrum and in marrow painted samples it is less intense than in M21, while ~4250 cm⁻¹ band is a shoulder of the band peaking at ~4325 cm⁻¹.

### 3.2.3. Multielement analysis by pEDXRF

X-ray fluorescence spectra and elemental concentration table are reported in the Supplementary Materials (SM5). The most relevant features of the rocks (SM5a) include intense Ca peaks for Kα (3.69 keV) and Kβ (4.01 keV) lines, which are more intense in LS2. Characteristic peaks of Sr (Kα = 14.16 keV, Kβ = 15.84 keV)

were also detected, as expected due to the marine sedimentary environment of these carbonate rocks. Peaks at Kα (6.40 keV) and Kβ (7.06 keV) emission lines of Fe are also present. Support LS3 differs from the other due to peaks linked to the presence of Ti (Kα = 4.51 keV), Zr (Kα = 15.77 keV, Kβ = 17.67 keV) and Rb (Kα = 13.40 keV). Weak peaks, possibly attributable to Kα lines of K (3.31 keV), Cr (5.40 keV), Mn (5.89 keV), Ni (7.06 keV), Cu (7.46 keV) and Zn (8.63 keV) can be also observed. Spectra from blank points (SM5b) of milk and (M22) and marrow (MA21) do not differ substantially from the one of their supports (LS1). The spectra of the points with the highest amount of pigment (M29, MA20 and W42; SM5c) show intense peaks for Fe (Kα = 6.40 keV, Kβ = 7.06 keV). Copper shows peaks for Kα line (7.46 keV) and, also, weak Kβ line is visible (8.91 keV). Titanium Kα line is suggested by asymmetry of Fe escape peak (4.66 keV) at 4.51 keV, furthermore, a weak peak at Kβ line is visible at least in M29 and M20 (4.93 keV). Ca peaks (Kα = 3.69 keV, Kβ = 4.01 keV) are less intense than those from bare rock (LS1 for M29 and MA20, and LS2 for W42). Lines for Sr seem less affected by the painted surface. The intensity of the other observed peaks does not seem to differ significantly from that of the bare support. The energy of the EDXRF lines correspond to the values reported by Bearden [69].

PCA was employed for data exploration (Fig 4). Variable selection is based on the spectral data and on the reported concentrations. K, Cr, Mn, Ni could be quantified in the pure pigment but could not be determined in most of the painted surfaces. Similar considerations could be done for Rb which was found at low levels in the pigment (21.0 ± 0.7 mg kg⁻¹) and in LS3 (19 ± 4 mg kg⁻¹) but was not detected in most of the samples. Zircon concentra-
<figure><img src="image_8.png" /><figcaption>**Fig. 4.** PCA diagrams of pEDXRF data: scores for PC1 vs PC2 (a) and variables/PCs correlation plot (b).</figcaption></figure>

tion is low in pigment (35 ± 3 mg kg⁻¹) and could be determined almost only in the samples from LS3, the only support with significant levels of this element (193 ± 111 mg kg⁻¹).

The first two PCs explain 65.7 % of the whole variance. The scores of samples from LS1 and LS2 increase on both PC1 and PC2 going from the bare supports and blanks (3rd quadrant) to the point the highest concentrations of Fe. The support LS3 falls far from the other rock slabs due to its peculiar chemical profile. It is worth noting that the samples from this particular support slightly deviate from the others and are plotted closer to LS3 in the fourth quadrant of the diagram.

Ca and Sr are both negatively correlated with PC1 indicating that they both are support elements. Fe and Cu cluster together in the correlation plot, which points out that their levels are driven by the pigment. Silicon and titanium group together and they are correlated in the positive direction to PC1, pointing out a link between these two elements and the pigment. However, the correlation in the negative direction with PC2 suggest a relevant contribution also of the support. In fact, LS3 is rich in these elements as well as LS3 samples. Aluminum is correlated only with PC2, evidencing that the detected concentrations are mainly driven by support features. Though Zn was detected in significant amounts only in the pigment, it does not show significant correlation with the two PCs, possibly because the low amount of this element in the samples and the closeness to the limits of detection compromise the accuracy of the quantification.

Finally, PCA could not evidence any influence of binding material on pEDXRF analysis.

### 3.2.4. Evaluation of the spectroscopy techniques

Raman could reveal the presence of Fe₂O₃ bands in most of the painted samples and calcite band from support LS2, but it could not detect organic compounds. Support nature strongly limited Raman analyses due to fluorescence. This phenomenon could be worsened on-field due to the presence of fluorescent superficial layers [7]. FT-NIR could also evidence the presence of iron ox-

ide and organic molecules from binders [70,71], and support characteristics could not mask features attributable to lipids in marrow samples and in milk blank (M22). Interestingly, these could not be identified in the other milk samples probably due to matrix effect. Although lipids are usually more resistant than other compounds [14], these sensitiveness limits should be considered in prehistoric case studies, where these materials are likely present in scarce amounts due to weathering and degradation [72]. Furthermore, fatty acids and other contaminants can be present in secondary layers [73].

PEDXRF was also influenced by the support. Data indicate that signal from pigment elements can be masked when the measurements are carried out on a painted rock surface. The role of support features has been pointed out also by PCA. Given the nature of this experiment, the effect of overlaying secondary coatings could not be controlled. However, studies indicate that their influence is particularly relevant for lightest elements [38].

### 3.3. Color analysis

#### 3.3.1. Color features

Result of analysis performed by smartphone colorimetry and spectroradiometer are reported in the Supplementary Materials (SM6a-b).

Chromatic coordinates show a standard deviation which in some cases exceed 10 % of the mean. It can be explained by the heterogeneity of the colored surfaces.

Values obtained by smartphone show slightly higher lightness than those obtained by spectroradiometer (Fig 5a). On the other hand, imaging gives slightly higher values for a* and b* than spectroradiometer. However, despite this shift, data by the two techniques are highly correlated, indicating the consistency of imaging values. Support points slightly deviate from the main correlation structure, especially for b*, possibly because the very same points could not be measured or because values are high for lightness and barely chromatic, reducing the accuracy of the method.
<figure><img src="image_9.png" /><figcaption>**Fig. 5.** Scatter plot of CIELAB coordinates obtained by smartphone (SM) and spectroradiometer (SP) with linear regression and coefficient of determination R² (a), and ΔE₀₀ distance between spectroradiometer results and predicted results of smartphone colorimetry obtained by polynomial regression employing all the points as training set (b), and by LOOCV (c). Red dashed line in Figs (b) and (c) indicates the proposed ΔE₀₀ tolerance limit of 3.5 for printed materials [56].</figcaption></figure>

Fig 6 shows the distribution of L*, a* and b* in selected samples measured by smartphone imaging colorimetry. Lightness (L*) decreases going from Points 0 (MA21, M22, LS2) to Points VI (MA20, M29, W42). On the contrary, chromatic values (a* and b*) increase from Points 0 (MA21, M22, LS2) to Points VI (MA20, M29, W42). This is due to the different contribution of the supports and the pigment. Lightness is higher in some water samples, but it could be due to their support, characterized by higher L* values (LS2: 74 ± 3) than the others (LS1: 56 ± 4). On the other hand, the high L* of milk samples, compared to marrow ones, could be caused by the contribution of milk compounds which determine its high lightness (L* ~80) [74]. The contribution in L* of milk is confirmed by comparing lightness of milk blank M22 (L* = 65 ± 9), and of its raw support LS1 (L* = 56 ± 4).

Water samples show higher levels of redness (a*) and yellowness (b*) in most of the points, although these parameters seem to plateau from Point III (W38) to Point VI (W42). While a* is similar in the two support pictures (LS1: 5.1 ± 0.7; LS2: 5.7 ± 0.9), b* is higher in LS1 (12.3 ± 0.9), the support of marrow and milk samples, than in LS2 (7.0 ± 1.0). Thus, it is unlikely that they are higher in water samples due to rock properties. Probably the addition of binding materials with lower chromaticity than the pigment determined mixtures with less saturated colors. In fact, previous studies suggest that milk levels of redness are close to 0, while b* is usually just slightly higher [74,75]. Comparing the points in which organic binders were used, higher redness and yellowness in most of the marrow samples than in the milk ones is consistent with the color properties of this material [76] and it is evidenced also by blank samples.

Color parameters for the replicates made with marrow (MA43-45), milk (M09-11) and water (W12-14; Supplementary Materials, SM6c) seem to confirm the above-quoted results. Milk samples have the highest lightness and the lowest a* and b*, while marrow samples have the lowest L*, despite being on the support with the highest one (LS2). Marrow paintings have slightly higher a* and b* than water.

Multivariate statistics was employed to further explore color data from imaging (Fig 7) in order to understand what kind of information could be obtained based only on these data.

Results of PCA are shown in Fig 7a and Fig 7b On PC1 axis we can distinguish between supports and blanks, and painted samples due to the high lightness of the former and the high chromaticity of the latter. Water samples can be distinguished from the others on PC2-axis, though the three replicates made with marrow (MA43-45) fall close to them, possibly due to the influence of the support (LS2). Finally, samples made with milk can be distinguished from the others due to their low PC1 and PC3 scores. Results from cluster analysis are complementary to the PCA. Sand blanks form a group apart in the dendrogram (Fig 7c). W36 is the most distant from the other painted samples. A first group of painted samples is mainly composed of milk samples, though a subcluster of marrow samples is also present. These samples (MA15-17) are those made with the lowest amount of pigment (from 0.0203 g to 0.0616 g) and the result is possibly linked to the high influence of the common support (LS1). The fourth cluster includes marrow and water paintings. Marrow samples with the highest amount of pigment (MA18-20; from 0.0784 to 0.1232 g) group apart in a subcluster, the three replicates (MA43-45) are instead closer to water samples.
<figure><img src="image_10.png" /><figcaption>Fig. 6. Distribution of L*, a* and b* for the sampled pixels of each point from Point 0 to Point VI.</figcaption></figure>

### 3.3.2. Potential use of in-lab made standards for smartphone camera characterization

The painted points were used to build polynomial regression models of degrees 1 to 3 employed to characterize the smartphone camera.

CIELAB coordinates of the colored samples were predicted based on models built using all the colored points as training set (Supplementary Materials, SM6d) and by LOOCV (Supplementary Materials, SM6e) to estimate the accuracy of the method.

The CIELAB values predicted by the three models are slightly closer to those obtained by spectroradiometer going from polynomial degree 1 to degree 3 (Fig 5b), though distributions are mostly overlapping, giving results that are in all the cases below the ΔE<sub>00</sub> threshold of 3.5. However, LOOCV (Fig 5c) suggests that polynomial degree 3 model is not robust, probably due to overfitting, while both degree 1-2 models produce satisfying results. Distances of lightness (ΔL*), chroma (ΔC*) and hue (ΔH*; Supplementary Materials, SM6f) do not show bias toward any direction, being evenly distributed around 0.

### 3.3.4. Evaluation of smartphone imaging colorimetry

Imaging and spectroradiometer data show a very high correlation. Also, the results of camera characterization point out the

feasibility of in-lab made calibration standards to obtain accurate CIELAB coordinates.

Color data are proxy of the amount of pigment employed. In fact, we saw that lightness, high in supports, decreases from Points 0 (MA21, M22, LS2) to Points VI (MA20, M29, W42; r is -0.75, *p* < 0.01, for L* vs Fe<sub>2</sub>O<sub>3</sub> mass). On the other hand, chromatic coordinates increase from Points 0 (MA21, M22, LS2) to Points VI (MA20, M29, W42) and they are correlated with Fe<sub>2</sub>O<sub>3</sub> mass (r is 0.76 for a* and 0.63 for b*, *p* < 0.01). Moreover, they also evidence differences linked to the binder.

Some *caveat* should be observed. First, results are influenced by rock substrate color, especially for the samples with low amounts of pigment. This must be considered when different contexts are compared or when the same support shows high color variability. Then, color can be altered by the formation of superficial mineral films covering the pictorial layer, due to the degradation of the original raw materials and to environmental factors, which can variably affect rock art remains also in the very same context [8,13,22]. Given the number of variables involved in determining the colors of a rock art panel, inferring the presence of organic compounds or identifying specific materials on the basis only of these data would be a hazardous and improper way of using this method, which is instead thought as a preliminary approach for
<figure><img src="image_11.png" /><figcaption>**Fig. 7.** Results of the exploratory analysis of color data: PC1 vs PC2 (top) and PC1 vs PC3 (bottom) score plots (a); correlations for PC1-3 and original variables (b); dogram for cluster analysis (c).</figcaption></figure>

<figure><img src="image_12.png" /><figcaption>**Fig. 8.** Samples/scores plot (a) and correlation plot for variables and PCs (b) for the PCA of smartphone colorimetry and pEDXRF data from Coco de la Gralla.</figcaption></figure>

carrying out a first documentation of a panel and planning follow-
ing fieldworks, or in case that moving analytical instrumentations
to the site would not be possible. Furthermore, the possible role of
smartphone colorimetry in the characterization and monitoring of
decay should be also considered [77].

4. Smartphone colorimetry on-field test at Coco de la Gralla Levantine rock art site

Results of image analysis at Coco de la Gralla are reported in
the Supplementary Materials (SM7a and SM7b). Data indicate that

rock have higher lightness than the painted points (L* = 70 ± 9 vs
50 ± 9), while painted points have higher redness than support
(a* = 22 ± 5 vs 8 ± 4). Yellowness is similar for rock and painting,
though average b* is slightly higher in the latter (b* = 33 ± 9)
than in the former (b* = 30 ± 6).

The relationship between colorimetric and elemental data pub-
lished by López-Montalvo and colleagues [40] was explored by PCA
(Fig 8). The first four components explain 79.5 % of the overall vari-
ance.

Painted surfaces have higher scores for PC1 than rock wall sur-
faces, while they have similar distributions along PC2 (Fig 8a), PC3
and PC4 axes (Supplementary Materials, SM7c). Correlation plot indicate that painted areas are characterized by relatively high values for a*. The rock surface seems instead to have higher lightness and b*, which show also a moderate correlation in the same direction respect to PC1. It is worth noticing that, except for 19.F1, fresh rock and calcite coatings spots have low PC2 compared to most samples of rock wall. These three points are indeed characterized by different colorimetric properties compared to the other rock wall samples, showing above average lightness and being less chromatic on both a* and b* axes.

The strong link among a*, Fe* and painted surface indicated by PC1 and PC2 is consistent with replicas data. Similarly, data of L*, indicating that higher lightness is a feature of the support, are in accord with laboratory experiment. However, Coco de la Gralla data gave insights on how weathering could have shaped the colorimetric features of rock-shelter and painted surface. In fact, b* values show a negative correlation with PC1, which is consistent with the high values observed on this axis, on the contrary of mock up slabs, which were significantly less chromatic than painted samples. Furthermore, variables clustering (b*, Al, Ti, Si on one side, and L*, S and maybe Sr on the others) could account for the different features of rock wall bare surface.

Pearson's correlation coefficients of color variables were calculated for painting points of the figures and the related closest bare wall point (when present). Lightness and b* show moderate and strong correlations respectively ($r_{L*} = 0.568, p < 0.05$; $r_{b*} = 0.861, p < 0.01$), while a* does not present a significant correlation ($r_{a*} = 0.350, p > 0.10$). These data suggests that colorimetric characteristic of painted points are influenced by the features of the rock wall area in which they are located, especially for b*. On the other hand, color properties seem to be less affected from the point of view of a* component, which is consistent with the role of pigment in this direction indicated by the experiment.

Biplots for L* vs a* and L* vs b* of painted points are reported in the Supplementary Materials (SM7d). For different figures color analysis evidenced relevant inner differences ($\Delta E_{00} = 8.03$ for 4.P1 and 5.P1, which are part of the same motif; $\Delta E_{00}$ is 7.78 for 7.P1-2; $\Delta E_{00}$ is 12.51 for 9.P1-2; $\Delta E_{00}$ is 17.97 for 17.P1-2). This is consistent with the context, characterized by weathered surface, in which the figures have been severely altered and often present highly heterogeneous surfaces. On the other hand, in some cases, motifs coming from the same area of the panel show similar color features also when they do not pertain neither to the same figure nor to the same variant class (e.g.: $\Delta E_{00}$ is 0.68 for 8.P1 and 9.P1; $\Delta E_{00}$ is 3.22 for 13.P1 and 14.P1-2) and it cannot be excluded that it is happening because of the influence of the rock wall and to similar alteration.

These results evidenced the difficulties of interpretation of colorimetric data due to their multifactorial character. This must be considered especially in contexts subjected to significant and variable degradation due to their exposure to weathering phenomena like Coco de la Gralla.

## 5. Conclusion

The non-invasive protocol carried out on the experimental replicas pointed out strength and limitations of the different techniques.

The combination of atomic and molecular spectroscopy techniques permitted to determine the elemental features of the pictorial layers, as well as the type of pigment (Raman and, in part, FT-NIR) and the presence of organic compounds (FT-NIR) in some of the samples. The most relevant limitations were linked to the interference of the signal from the support and to the limits of detection of the instruments.

As regards smartphone imaging colorimetry, the test carried out in this study suggests that it can provide accurate color data, comparable to those obtained by reference devices such as spectro-radiometers and spectrocolorimeters just by employing appropriate standards as training set for camera characterization. Data inner consistency indicates that this technique can reveal difference within the same rock art panel, possibly driven by raw materials, support features or conservation state.

The pilot study carried out at Coco de la Gralla Levantine rock art panel show correlations between chemical features and smartphone colorimetry which are in part consistent with laboratory experiment but evidencing features possibly related to the context. Data indicate significant correlations between painting and close support colorimetric features, suggesting that color data are driven by different factors, which include both support color and weathering phenomena to which surface has been subjected within the rock-shelter.

Together, the laboratory and the first on-field test indicated that smartphone imaging colorimetry could be a convenient, easily portable, user friendly and cheap alternative to carry out preliminary survey at rock art sites or for monitoring their conservation state through the change in color over time. The collected data can be used to document color variability in a panel, in order to characterize rock wall and pictorial surface, observing possible clustering, and properly plan subsequent fieldwork campaigns and sampling operations, or in case that performing more in-depth archeometric studies would be impossible. However, the number of variables which can influence the results of this technique pointed out the challenges posed by the interpretation of these data, suggesting great caution in reaching too univocal conclusions on the studied materials without the support of other well-established traditional analytical techniques.

## Acknowledgments

The research on Coco de la Gralla site was developed under permits by the Archaeology and Palaeontology Service of the Catalan Government (File 511 K121 411-2018-2-22093). The authors thank Tània Álvarez Herraiz, Maria Teresa Miró i Alaix and Josep Castells (Service for Archaeological and Palaeontological Heritage, General Direction of Cultural Heritage, Catalan Government), Pepa Subirats (Centre of Rural Development and Pauma Museum, Municipality of Mas de Barberans), Joan Martínez Tomás (Territorial Service of Culture in the Ebro Territory) and the Municipality of Mas de Barberans (Tarragona, Spain).

The authors would like to thank the anonymous Reviewers for their helpful suggestions. MR acknowledges the support of the Ministry of Education, Culture, Universities and Occupation of the Valencian Government and the European Social Fund Plus (FSE+) for funding the APOSTD postdoctoral contract (CIAPOS/2023/115). MLC and AMR thank the Valencian Government for funding of the Prometeo project "Smart Chemical Lab" (CIPROM/2023/37). GG is grateful to the Spanish Ministry of Science and Innovation for funding the project EvolMED "Evolutionary cultural patterns in the contexts of the neolithization process in the Western Mediterranean" (PID2021-127731NB-C21). Finally, ELM acknowledges the support of the National Agency of Research of France which funded the project HOLOGRAM (ANR-22-CE27-0021).

## Supplementary materials

Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.culher.2025.10.019.
## References

[1] E. López-Montalvo, C. Roldán, E. Badal, S. Murcia-Mascarós, V. Villaverde, Identification of plant cells in black pigments of prehistoric Spanish Levantine rock art by means of a multi-analytical approach: a new method for social identity materialization using chaîne opératoire, PLoS One 12 (2017) e0172225, doi:10.1371/journal.pone.0172225.

[2] I. Domingo, A. Chieli, Characterizing the pigments and paints of pre-historic artists, Archaeol. Anthropol. Sci. 13 (2021) 196, doi:10.1007/s12520-021-01397-y.

[3] M. Batıashvili, M. Gallinaro, F.B. Restelli, L. Medeghini, C. Young, M. Botticelli, Colour of the past in South Caucasus: the first archaeometric investigation on rock art and pigment residues from Georgia, Quat. Int. 658 (2023) 1–13, doi:10.1016/j.quaint.2023.03.019.

[4] J. Aramendia, S.F.-O. De Vallejuelo, M. Maguregui, I. Martinez-Arkarazo, A. Giakoumakaki, A.P. Martí, J.M. Madariaga, J.F. Ruiz, Long-term in situ non-invasive spectroscopic monitoring of weathering processes in open-air prehistoric rock art sites, Anal. Bioanal. Chem. 412 (2020) 8155–8166, doi:10.1007/s00216-020-02949-2.

[5] M. Gallinaro, A. Zerboni, Rock, pigments, and weathering: A preliminary assessment of the challenges and potential of physical and biochemical studies on rock art from southern Ethiopia, Quat. Int. 572 (2021) 41–51, doi:10.1016/j.quaint.2020.05.056.

[6] A. Bonneau, D.G. Pearce, A.M. Pollard, A multi-technique characterization and provenance study of the pigments used in San rock art, South Africa, J. Archaeol. Sci. 39 (2012) 287–294, doi:10.1016/j.jas.2011.09.011.

[7] A. Hernanz, J.F. Ruiz-López, J.M. Madariaga, E. Gavrilenko, M. Maguregui, S. Fdez-Ortiz De Vallejuelo, I. Martínez-Arkarazo, R. Allozo-Izquierdo, V. Baldellou-Martínez, R. Viñas-Vallverdú, A. Rubio I Mora, À. Pitarch, A. Giakoumakaki, Spectroscopic characterisation of crusts interstratified with prehistoric paintings preserved in open-air rock art shelters, J. Raman Spectrosc. 45 (2014) 1236–1243, doi:10.1002/jrs.4535.

[8] L. Rampazzi, Calcium oxalate films on works of art: a review, J. Cult. Herit. 40 (2019) 195–214, doi:10.1016/j.culher.2019.03.002.

[9] J. Huntley, M. Aubert, A.A. Oktaviana, R. Lebe, B. Hakim, B. Burhan, L.M. Aksa, I.M. Gería, M. Ramli, L. Siagian, H.E.A. Brand, A. Brumm, The effects of climate change on the pleistocene rock art of Sulawesi, Sci. Rep. 11 (2021) 9833, doi:10.1038/s41598-021-87923-3.

[10] L. Mol, H.A. Viles, Geoelectric investigations into sandstone moisture regimes: implications for rock weathering and the deterioration of San Rock Art in the Golden Gate Reserve, South Africa, Geomorphology 118 (2010) 280–287, doi:10.1016/j geomorph.2010.01.008.

[11] A. Zerboni, F. Villa, Y.-L. Wu, T. Solomon, A. Trentini, A. Rizzi, F. Cappitelli, M. Gallinaro, The sustainability of rock art: preservation and research, Sustain 14 (2022) 6305, doi:10.3390/su14106305.

[12] E. Chalmin, S. Hørlé, I. Reiche, Taphonomy on the surface of the Rock Wall: rock-paint-atmosphere interactions, in: B. David, I.J. McNiven (Eds.), The Oxford Handbook of the Archaeology and Anthropology of Rock Art, Oxford University Press, 2017, doi:10.1093/oxfordhb/9780190607357.013.47.

[13] I.L. Ozán, S. Oriolo, L. Gutiérrez, A.C. Esnal, A. Latorre, M.A. Castro, A. Fazio, Rock art painting taphonomy: the role of environmental and technological factors, J. Archaeol. Method Theory 31 (2024) 782–821, doi:10.1007/s10816-023-09619-4.

[14] I. Domingo Sanz, M. Vendrell, A. Chieli, A critical assessment of the potential and limitations of physicochemical analysis to advance knowledge on Levantine rock art, Quat. Int. 572 (2021) 24–40, doi:10.1016/j.quaint.2020.09.020.

[15] M. Sepúlveda, Making visible the invisible. A microarchaeology approach and an Archaeology of Color perspective for rock art paintings from the southern cone of South America, Quat. Int. 572 (2021) 5–23, doi:10.1016/j.quaint.2020.05.031.

[16] A. Bonneau, D.G. Pearce, A.M. Pollard, A multi-technique characterization and provenance study of the pigments used in San rock art, South Africa, J. Archaeol. Sci. 39 (2012) 287–294, doi:10.1016/j.quaint.2020.05.031.

[17] N.S. Da Rosa, D. Fiore, R. Viñas, Testing recipes: an experimental approach to paint production processes in Levantine rock art (Spain), Archaeometry 65 (2023) 816–832, doi:10.1111/arcm.12856.

[18] A. Tournié, L.C. Prinsloo, C. Paris, P. Colomban, B. Smith, The first in situ Raman spectroscopic study of San rock art in South Africa: procedures and preliminary results, J. Raman Spectrosc. 42 (2011) 399–406, doi:10.1002/jrs.2682.

[19] N. Carden, L. Miotti, Unraveling rock art palimpses through superimpositions: the definition of painting episodes in Los Toldos (southern Patagonia) as a baseline for chronology, J. Archaeol. Sci. Rep. 30 (2020) 102265, doi:10.1016/j.jasrep.2020.102265.

[20] M. Gerardin, N. Holzschuch, A. Ibanez, B. Schmitt, P. Martinetto, Influence of micro-structural features on the colour of nanocrystallised powders of hematite and visible-NIR reflectance spectra simulations, J. Int. Col. Assoc. 16 (2021) 41–48.

[21] A.L. Watchman, Age and composition of oxalate-rich crusts in the Northern Territory, Australia, Stud. in Conserv. 36 (1991) 24–32, doi:10.1179/sic.1991.36.1.24.

[22] Y.-L. Wu, F. Villa, G. Mugnai, M. Gallinaro, E.E. Spinapolice, A. Zerboni, Geomicrobial investigations of colored outer coatings from an Ethiopian rock Art Gallery, Coat 10 (2020) 536, doi:10.3390/coatings10060536.

[23] K. Hall, I. Meiklejohn, J. Arocena, The thermal responses of rock art pigments: implications for rock art weathering in southern Africa, Geomorphology 91 (2007) 132–145, doi:10.1016/j geomorph.2007.02.002.

[24] J.F. Ruiz, J. Pereira, The colours of rock art. Analysis of colour recording and communication systems in rock art research, J. Archaeol. Sci. 50 (2014) 338–349, doi:10.1016/j.jas.2014.06.023.

[25] N. Carden, L. Miotti, Unraveling rock art palimpses through superimpositions: the definition of painting episodes in Los Toldos (southern Patagonia) as a baseline for chronology, J. Archaeol. Sci. Rep. 30 (2020) 102265, doi:10.1016/j.jasrep.2020.102265.

[26] D. Lafon-Pham, S. Konik, J. Monney, On-site spectroradiometric analysis of palaeolithic cave art: investigating colour variability in the red rock art of Points cave (France), J. Archaeol. Sci. Rep. 42 (2022) 103384, doi:10.1016/j.jasrep.2022.103384.

[27] L.C. Bloch, J.D. Hosen, E.C. Kracht, M.J. LeFebvre, C.J. Lopez, R. Woodcock, W.F. Keegan, Is it better to be objectively wrong or subjectively right?: testing the accuracy and consistency of the Munsell capsule spectrocolorimeter for archaeological applications, Adv. Archaeol. Pract. 9 (2021) 132–144, doi:10.1017/aap.2020.53.

[28] R.R. Gerharz, R. Lantermann, D.R. Spennemann, Munsell color charts: a necessity for archaeologists? Aust. J. Hist. Archaeol. 6 (1988) 88–95.

[29] A. Molada-Tebar, J.L. Lerma, Á. Marqués-Mateu, Camera characterization for improving color archaeological documentation, Color. Res. Appl. 43 (2018) 47–57, doi:10.1002/col.22152.

[30] A. Molada-Tebar, Á. Marqués-Mateu, J.L. Lerma, S. Westland, Dominant color extraction with K-means for camera characterization in cultural heritage documentation, Remote Sens. 12 (2020) 520, doi:10.3390/rs12030520.

[31] E. Quesada, J. Harman, A step further in rock art digital enhancements, DStretch on gigapixel imaging, Digit. Appl. Archaeol. Cult. Herit. 13 (2019) e00098, doi:10.1016/j.daach.2019.e00098.

[32] E. Cerrillo-Cuenca, M. Sepúlveda, An assessment of methods for the digital enhancement of rock paintings: the rock art from the precordillera of Arica (Chile) as a case study, J. Archaeol. Sci. 55 (2015) 197–208, doi:10.1016/j.jas.2015.01.006.

[33] E. Cerrillo-Cuenca, M. Sepúlveda, G. Cabello, F. Bastías, Color-based discrimination of color hues in rock paintings through gaussian mixture models: a case study from Chomache site (Chile), Herit. Sci. 12 (2024) 169, doi:10.1186/s40494-024-01239-9.

[34] C. Roldán, S. Murcia-Mascarós, J. Ferrero, V. Villaverde, E. López, I. Domingo, R. Martínez, P.M. Guillem, Application of field portable EDXRF spectrometry to analysis of pigments of Levantine rock art, X-ray Spectrom. 39 (2010) 243–250, doi:10.1002/xrs.1254.

[35] À. Pitarch, J.F. Ruiz, S. Fdez-Ortiz de Vallejuelo, A. Hernanz, M. Maguregui, J.M. Madariaga, In situ characterization by Raman and X-ray fluorescence spectroscopy of post-paleolithic blackish pictographs exposed to the open air in Los Chaparros shelter (Albalate del Arzobispo, Teruel, Spain), Anal. Methods 6 (2014) 6641, doi:10.1039/C4AY00539B.

[36] G. Mauran, M. Lebon, F. Détroit, B. Caron, A. Nankela, D. Pleurdeau, J.-J. Bahain, First in situ pXRF analyses of rock paintings in Erago, Namibia: results, current limits, and prospects, Archaeol. Anthropol. Sci. 11 (2019) 4123–4145, doi:10.1007/s12520-019-00787-7.

[37] M.O. Andreae, T.W. Andreae, J.E. Francis, L.L. Loendorf, Age estimates for the rock art at the Rocky Ridge site (UT) based on archaeological and archaeometric evidence, J. Archaeol. Sci. Rep. 48 (2023) 103875, doi:10.1016/j.jasrep.2023.103875.

[38] E. López-Montalvo, V. Villaverde, C. Roldán, S. Murcia, E. Badal, An approximation to the study of black pigments in Cova Remigia (Castellón, Spain), Technical and cultural assessments of the use of carbon-based black pigments in Spanish Levantine Rock art, J. Archaeol. Sci. 52 (2014) 535–545, doi:10.1016/j.jas.2014.09.017.

[39] C. Roldán, S. Murcia-Mascarós, E. López-Montalvo, C. Vilanova, M. Porcar, Proteomic and metagenomic insights into prehistoric Spanish Levantine Rock art, Sci. Rep. 8 (2018) 10011, doi:10.1038/s41598-018-21216-1.

[40] E. López-Montalvo, G. Galletlo, C. Roldán, M. Ramacciotti, E. Goemaere, A.M. Rubio, S. Murcia-Mascarós, P. Subirats, New insights into the Spanish Levantine rock art pigments combining pXRF and stylistic approach: the Coco de la Gralla site (Mas de Barberans, Tarragona, Spain) as a case study, J. Archaeol. Sci. Rep. 66 (2025) 105273, doi:10.1016/j.jasrep.2025.105273.

[41] A. Rousaki, C. Vázquez, V. Aldazábal, C. Bellelli, M. Carballido Calatayud, A. Hajduk, E. Vargas, O. Palacios, P. Vandenabeele, L. Moens, The first use of portable RAMAN instrumentation for the in situ study of prehistoric rock paintings in Patagonian sites, J. Raman Spectrosc. 48 (2017) 1459–1467, doi:10.1002/jrs.5107.

[42] A. Rousaki, E. Vargas, C. Vázquez, V. Aldazábal, C. Bellelli, M. Carballido Calatayud, A. Hajduk, O. Palacios, L. Moens, P. Vandenabeele, On-field Raman spectroscopy of Patagonian prehistoric rock art: pigments, alteration products and substrata, TrAC Trends. Anal. Chem. 105 (2018) 338–351, doi:10.1016/j.trac.2018.05.011.

[43] I. Rudner, Paints of the Khoisan rock artists, Goodwin Ser. 4 (1983) 14, doi:10.2307/3858097.

[44] P. Villa, L. Pollarolo, I. Degano, L. Birolo, M. Pasero, C. Biagioni, K. Douka, R. Vinciguerra, J.J. Lucejko, L. Wadley, A milk and ochre paint mixture used 49,000 years ago at Sibudu, South Africa, PLoS One 10 (2015) e0131273, doi:10.1371/journal.pone.0131273.

[45] N. Santos da Rosa, La tecnología del arte rupestre levantino, Rev. Cuad. Arte Prehist. 7 (2019) 120–146.

[46] A. Watchman, Perspectives and potentials for absolute dating prehistoric rock paintings, Antiq 67 (1993) 58–65, doi:10.1017/S0003598X00045051.
[47] M. Ramacciotti, G. Gallello, M. Lezzerini, S. Pagnotta, A. Aquino, L. Alapont, J. Antonio Martín Ruiz, A. Pérez-Malumbres Landa, R. Hiraldo Aguilera, D. Godoy Ruiz, A. Morales-Rubio, M. Luisa Cervera, A. Pastor, Smartphone application for ancient mortars identification developed by a multi-analytical approach, J. Archaeol. Sci. Rep. 43 (2022) 103433, doi:10.1016/j.jasrep.2022.103433.

[48] M. Ramacciotti, G. Gallello, S. Columbu, D. Fancello, A. Diez-Castillo, O. García-Puchol, A. Pastor, M.L. Cervera, Smartphone photos as a non-destructive approach to characterise siliceous artefacts, STAR: Sci. Technol. Archaeol. Res. 9 (2023) e2240175, doi:10.1080/20548923.2023.2240175.

[49] R. Sáez-Hernández, K.U. Antela, G. Gallello, M.L. Cervera, A.R. Mauri-Aucejo, A smartphone-based innovative approach to discriminate red pigments in roman frescoes mock-ups, J. Cult. Herit. 58 (2022) 156-166, doi:10.1016/j.culher.2022.10.003.

[50] R. Sáez-Hernández, M.L. Cervera, Á. Morales-Rubio, M.J. Luque, I. Pérez-Torralba, G. Gallello, K.U. Antela, A.R. Mauri-Aucejo, Digital image-based method to identify historical pigments in wall paintings, Dyes. Pigm. 222 (2024) 111912, doi:10.1016/j.dyepeg.2023.111912.

[51] R. Sáez-Hernández, M.J. Luque, A.R. Mauri-Aucejo, Á. Morales-Rubio, M.L. Cervera, A two-way analytical investigation of ancient gold coins: elemental and colorimetric description of precious materials, Heliyon 10 (2024) e34623, doi:10.1016/j.heliyon.2024.e34623.

[52] M. Ramacciotti, G. Gallello, E. López-Montalvo, A. Morales-Rubio, C. Roldán, Aplicación de smartphone en el análisis colorimétrico de réplicas de pinturas rupestres, in: F.J. Burgos Fernández, M. Vilaseca Ricart (Eds.), XIII Congreso Nacional Del Color: Terrassa, Universitat Politècnica de Catalunya, 2022, pp. 61-64. http://hdl.handle.net/2117/373277.

[53] J. Malo, M.J. Luque, COLORLAB: a Color Processing Toolbox for Matlab, Universitat de València, 2002 https://www.uv.es/vista/vistavalencia/software/colorlab.html.

[54] M.D. Fairchild, Color Appearance Models, 1st ed., Wiley, 2013, doi:10.1002/9781118653128.

[55] M.R. Luo, G. Cui, B. Rigg, The development of the CIE 2000 colour-difference formula: CIEDE2000, Col. Res. Appl. 26 (2001) 340-350, doi:10.1002/col.1049.

[56] H.X. Liu, B. Wu, Y. Liu, M. Huang, Y.F. Xu, A discussion on printing color difference tolerance by CIEDE2000 color difference formula, Appl. Mech. Mat. 262 (2012) 96-99, doi:10.4028/www.scientific.net/AMM.262.96.

[57] R Core TeamR: A Language and Environment for Statistical Computing, 2024 https://www.R-project.org/.

[58] H. Wickham, ggplot2: Elegant Graphics for Data Analysis, Springer, New York, NY, 2009, doi:10.1007/978-0-387-98141-3.

[59] R.G. Brereton, Principal components analysis: row scaling and compositional data, J. Chemom. 39 (2025) e3606, doi:10.1002/cem.3606.

[60] I.M. Bell, R.J.H. Clark, P.J. Gibbs, Raman spectroscopic library of natural and synthetic pigments (pre- ≈ 1850 AD), Spectrochim. Acta Part A: Molec. Biomol. Spectrosc. 53 (1997) 2159-2179, doi:10.1016/S1386-1425(97)00140-6.

[61] H.G.M. Edwards, L. Drummond, J. Russ, Fourier-transform raman spectroscopic study of pigments in native American Indian rock art: seminole Canyon, Acta Part A: Molec. Biomol. Spectrosc. 54 (1998) 1849-1856, doi:10.1016/S1386-1425(98)00077-8.

[62] R.N. Clark, Spectroscopy of rocks and minerals, and principles of spectroscopy, in: Manual of Remote Sensing, 3, Wiley, NY Weinheim, 1999, pp. 3-52.

[63] B. Antony, S. Sharma, B.M. Mehta, K. Ratnam, K.D. Aparnathi, Study of fourier transform near infrared (FT-NIR) spectra of ghee (anhydrous milk fat), Int. J. Dairy Tech. 71 (2018) 484-490, doi:10.1111/1471-0307.12450.

[64] M.M. Cascant, C. Breil, A.S. Fabiano-Tixier, F. Chemat, S. Garrigues, M. De La Guardia, Determination of fatty acids and lipid classes in salmon oil by near infrared spectroscopy, Food Chem. 239 (2018) 865-871, doi:10.1016/j. foodchem.2017.06.158.

[65] R.N. Clark, T.V.V. King, M. Klejwa, G.A. Swayze, N. Vergo, High spectral resolution reflectance spectroscopy of minerals, J. Geophys. Res. 95 (1990) 12653-12680, doi:10.1029/JB095iB08p12653.

[66] S.J. Gaffey, Spectral reflectance of carbonate minerals in the visible and near infrared (0.35-2.55 µm): anhydrous carbonate minerals, J. Geophys. Res. 92 (1987) 1429-1440, doi:10.1029/JB092iB02p01429.

[67] R.A. Viscarra Rossel, R.N. McGlynn, A.B. McBratney, Determining the composition of mineral-organic mixes using UV-vis-NIR diffuse reflectance spectroscopy, Geoderma 137 (2006) 70-82, doi:10.1016/j.geoderma.2006.07.004.

[68] S.E. Kays, W.R. Windham, F.E. Barton, Prediction of total dietary Fiber by near-infrared reflectance spectroscopy in high-fat- and high-sugar-containing cereal products, J. Agric. Food Chem. 46 (1998) 854-861, doi:10.1021/jf9706484.

[69] J.A. Bearden, X-ray wavelengths, Rev. Mod. Phys. 39 (1967) 78-124, doi:10.1103/RevModPhys.39.78.

[70] F. Rosi, L. Cartechini, D. Sali, C. Miliani, Recent trends in the application of Fourier Transform Infrared (FT-IR) spectroscopy in Heritage Science: from micro- to non-invasive FT-IR, Phys. Sci. Rev. 4 (2019), doi:10.1515/psr-2018-0006.

[71] K.R. Horn, G. Walker, V. Winton, E. Ramanaidou, C. Hamlett, B. Hamlett, Field characterisation of rock art paintings using non-invasive reflectance spectroscopy in the search for organic paint binders at Genealogy and Stickman Rockshelters in the Weld Range (Western Australia), J. Archaeol. Sci. Rep. 34 (2020) 102617, doi:10.1016/j.jasrep.2020.102617.

[72] J. Linderholm, P. Geladi, N. Gorretta, R. Bendoula, A. Gobrecht, Near infrared and hyperspectral studies of archaeological stratigraphy and statistical considerations, Gearchaeology 34 (2019) 311-321, doi:10.1002/gea.21731.

[73] S. Spades, J. Russ, GC-MS analysis of lipids in prehistoric rock paint and associated oxalate coatings from the lower Pecos region, Texas, Archaeometry 47 (2005) 115-126, doi:10.1111/j.1475-4754.2005.00191.x.

[74] A. McDermott, G. Visentin, S. McParland, D.P. Berry, M.A. Fenelon, M. De Marchi, Effectiveness of mid-infrared spectroscopy to predict the color of bovine milk and the relationship between milk color and traditional milk quality traits, J. Dairy Sci. 99 (2016) 3267-3273, doi:10.3168/jds.2015-10424.

[75] V.A. Solah, V. Staines, S. Honda, H.A. Limley, Measurement of milk color and composition: effect of dietary intervention on Western Australian Holstein-friesian cow's milk quality, J. Food Sci. 72 (2007), doi:10.1111/j.1750-3841.2007.00491.x.

[76] R.A. Mancini, M.C. Hunt, K.A. Hachmeister, D.H. Kropf, D.E. Johnson, Exclusion of oxygen from modified atmosphere packages limits beef rib and lumbar vertebrae marrow discoloration during display and storage, Meat Sci. 69 (2005) 493-500, doi:10.1016/j.meatsci.2004.09.003.

[77] R. Brigham, J. Grau-Bové, A. Rudnicka, M. Cassar, M. Strlic, Crowdsourcing as an analytical method: metrology of smartphone measurements in heritage science, Angewandte Chemie 130 (2018) 7545-7549, doi:10.1002/ange.201801743.