Skip to main content
Heritage Science
Download PDF
Download ePub

Visualization and mapping of literature on the scientific analysis of wall paintings: a bibliometric analysis from 2011 to 2021

Heritage Science volume 10, Article number: 105 (2022) Cite this article

Abstract

As non-renewable cultural heritage, wall paintings play an important role in society. To reveal the trends in the scientific analysis of mural paintings, 845 relevant research articles published from 2011 to 2021 were collected from the Web of Science database and analyzed. The VOSviewer software was adopted to map the network data of scientific publications, so that relationships among authors, countries, institutions can be displayed, and the co-occurrence of keywords and co-citation can be analyzed. The results revealed close and strong interconnections between the top authors, suggesting a considerable strong research link in this field. The cooperation between research institutions was relatively close. The most productive country of relevant publications was Italy. The leading journals for the scientific analysis of wall paintings were Journal of Raman Spectroscopy and Journal of Cultural Heritage. At present, the hotspots of scientific analysis and research on wall painting are revealing the composition, distribution, origin, and deterioration mechanism of pigments, alongside with evaluating the effects and mechanism of conservation materials and techniques. On the one hand, a possible development direction in this field is introducing more cutting-edge analysis and data processing methods. On the other hand, scientific analysis is increasingly adopted to guide the research and development of mural conservation materials.

Introduction

Wall paintings are one of the earliest painting forms in human history, tracing back to the late Paleolithic period. The earliest examples include the mural of Chauvet Cave in Ardèche, southern France. Many other ancient murals were also preserved, including the ones found in the ancient tombs of the Valley of the Kings, the palaces of Crete civilization, and Pompeii [1]. The development of wall paintings is closely connected with the customs, religion, philosophy, and aesthetics of different nationalities in various historical periods. As precious non-renewable cultural heritage, the production of murals is also compatible with the political, economic, cultural, and technological development of certain societies at certain times [2].

Affected by natural and human factors, wall paintings were damaged to varying degrees over time. Following the principle of minimum intervention, analysis of wall paintings adopts various scientific and technological methods to investigate the materials and techniques of ancient murals, assess their preservation status, and prepare for future restoration and preventive conservation. The analysis of ancient mural samples began in 1800 when Haslam examined samples of English medieval wall paintings and characterized 6 different pigments [3]. In 1814, Davy analyzed the various pigments in murals from Rome [4]. With the development of chromatography and spectroscopy in the 1950s, traditional chemical methods have been gradually replaced by modern analytical methods [5]. In the mid-1980s, Guineau applied Raman spectroscopy to analyze murals [6, 7], which later played an important role in the mural analysis [8]. In the 1990s, FT-IR was recognized as a very useful instrumental technique in the analysis and technical examination of wall paintings [9]. It provides a method of analysis which does not require much complex preparation of samples. With technological advancements, many researchers in chemistry, biology, and materials science carried out scientific analysis of mural paintings, which promoted the continuous development of theories and technologies in this field and the accumulation of research articles [10,11,12,13,14].

There are more than 10 commonly used methods for the scientific analysis of wall paintings. Chemical analysis methods, X-ray fluorescence (XRF), Raman spectroscopy (RS), and polarized light microscopy (PLM) have been adopted to detect mural pigments and clarify their material composition. Pyrolysis gas chromatography mass spectrometry (Py-GC/MS), liquid chromatography mass spectrometry (LC/MS), proteomics, immunology, and other analytical techniques have been applied to analyze the binding materials. X-ray diffraction (XRD) and scanning electron microscopy (SEM) have been used to analyze the structure of the ground layer [15,16,17,18,19,20]. Comprehensive application of the various analytical methods can help determine the mechanisms of deteriorations and offer important theoretical support to develop targeted conservation measures.

Scientific analyses on wall paintings have been increasing in recent years. Bibliometric research can objectively and comprehensively reveal the development and trends in a field and help fellow researchers quickly understand the research focus. VOSviewer is developed by Nees Jan van Eck and Ludo Waltman of Leiden University in the Netherlands for mapping and visualizing econometric networks. It can display the development, research focus, and trends of a certain discipline within a certain period and reveal the evolution of multiple research frontiers [21].

In this paper, the relevant literatures published between 2011 and 2021 were collected from the Web of Science database, then bibliometrics and knowledge graph analysis were conducted on the VOSviewer software. The situation of scientific analysis on wall paintings in the past decade was visualized. The relevant literatures were quantitatively analyzed to form the corresponding knowledge map, identify the knowledge base of the research area, and provide the latest progress of related research, frontiers, hotspots, evolution paths, and future development trends of the scientific analysis of wall paintings. This study could promote further development in the scientific analysis of wall paintings.

Methodology

The bibliometric analysis combines mathematics, statistics, and other measurement methods to study the distribution structure, quantitative relationship, and variation pattern of literature. In addition to articles and books, its analysis objects also include the relevant information within the article, such as the title, subject terms, keywords, word frequency, co-citation, co-occurrence, citation information, co-cited references, citation coupling, author, collaborator, publisher, date, language, institution, and country, thus can be used to analyze the research overview and development trend of a subject [22].

Taking the Web of Science database as the data source, the search formula of (TS = material OR TS = characterization) AND (TS = mural painting OR TS = wall painting OR TS = architectural painting OR TS = rock art painting) was adopted to collect articles published from 2011 to 2021. The article type filter was set to journal articles, and the language filter was set to English. The collected articles with Full Record and Cite References were then saved as plain text for subsequent analysis. The original data were imported into an Excel spreadsheet, and the year of publication, language, title, and article type of all the articles were checked manually. Items were rejected if they were not published in English, or without the 2011–2021 period, or on an irrelevant topic. The flow chart of the search strategy was shown in Fig. 1, and finally a dataset with 845 articles was formed.

The final dataset was imported into VOSviewer to obtain the bibliometric analysis graphs, where a circle and label represent a node, and a larger circle represents a higher level of importance. The same color signifies the same cluster. The node types in this study included authors, countries, institutions, journals, and keywords. The corresponding cooperation network analysis, co-citation analysis, and co-occurrence analysis were conducted.

Fig. 1
figure 1

Flow chart of the search strategy

Full size image

Results and discussion

Analysis of publications

Changes in the number of published articles of a specific research direction directly reflect the variation of research outputs within a specific period. Therefore, it is an essential indicator of the development trend in that period. It is of great significance for analyzing the dynamics and trends of future research and development [23].

Figure 2 shows the evolution in the number of published articles and the significant variations within this research field during the whole study period. Overall, the number of publications in this field is increasing steadily year by year. The highest productivity was observed in 2021 with a total of 152 papers, whereas the lowest was in 2011 with a total of 33. It can be seen that the number of literatures increased rapidly from 2020 to 2021. The COVID-19 pandemic in 2020 may has a bearing on this situation, as it has led to the postponement of some cultural heritage projects till 2021.

Fig. 2
figure 2

The number of published papers on scientific analysis of murals (2011–2021)

Full size image

Article network analysis

Analysis of author cooperation relationship

The co-authorship network of publications in mural scientific analysis from 2011 to 2021 revealed 3279 authors. Under the threshold of at least 3 published articles per author with 3 citations per author, 199 authors were identified, but only 105 authors were visually mapped in Fig. 3 as some of them were not interconnected.

Citation is the most frequent method used as a measure of the influence of an author or a paper because it can quickly identify important works in the field [24, 25]. Table 1 showed the top 10 authors by citations, namely Maguregui, Madariaga, Castro, Martinez-Arkarazo, de Vallejuelo, Veneranda, Bersani, Detalle, Giakoumaki, and Osanna. Seven of the top 10 authors are from Spain, indicating that Spain has a major contribution to the scientific analysis of wall paintings. These influential authors mainly focused on chemistry, spectroscopy, and materials science.

As shown in Fig. 3, the lines among the authors represent their cooperation links, and the 8 different colors represent the author collaboration clusters. It can be noted that authors from the same country were closely related. There were also cross-border cooperations, such as Holakooei from Iran has a partnership with Casoli from Italy in the C3 cluster, Vandenabeele from Belgium has a partnership with Mirao from Portugal in the C4 cluster.

Fig. 3
figure 3

Co-authorship network of scientific analysis of murals (2011–2021)

Full size image
Table 1 Top 10 authors of mural scientific analysis (ranking by citations)
Full size table

Analysis of country cooperation

From 2011 to 2021, there were 76 countries involved in the research on the scientific analysis of murals. Under the threshold of at least 5 articles published per country with a minimum of 5 citations, 36 countries were selected, resulting in a country cooperation relationship map with 145 links and a total link strength of 374. The top 5 countries of relevant publications are Italy (236), Spain (142), China (82), USA (73), France (63).

As shown in Fig. 4, each country is represented by a node sized proportionally to its number of publications. The lines connecting the nodes show the existing interconnection among the countries. The largest node diameters manifested by Italy and Spain indicate that they are the main countries in mural scientific analysis by absolute influence. This may be linked to the fact that both countries have the largest cultural heritage remains in Europe. Among the top 10 countries by node diameter, there are 8 countries from Europe, indicating that European scholars are leading in this field. As shown in Table 2, the country with the highest number of citations is Italy (3092), followed by Spain (1736) and France (978). Although China ranks third by the number of publications, its ranking is not high in the co-authorship network. The reason may be that Chinese scholars focused largely on domestic academic exchanges.

Fig. 4
figure 4

Co-authorship network map of countries publishing on scientific analysis of murals (2011–2021)

Full size image
Table 2 Top 10 countries contributed to the scientific analysis of murals (ranking by articles)
Full size table

Institutional analysis

From 2011 to 2021, 1173 research institutions were involved in the scientific analysis of murals. A total of 79 research institutions reached the threshold of 5. However, only 76 institutions are shown on the map because some have no cooperative relationship. The number of links in the institution cooperation map is 165, and the total link strength is 272 (Fig. 5). Table 3 lists the top 10 research institutions in the scientific analysis of wall paintings: CNR (42), University of the Basque Country (28), Cairo University (26), University of Pisa (20), University of Evora (17), University of Granada (16), Universidad Nacional Autonoma de Mexico (15), University of Seville (15), Dunhuang Academy (14), and University of Florence (14).

In density diagram, each point has a color that indicates the density of items at that point. The redder color indicates higher number and importance of items in the neighborhood of a point. So, it can be used to observe the density of knowledge and research in a certain field. As shown in Fig. 6, the frontier of mural scientific analysis was mainly concentrated in universities and local research institutions relying on rich cultural heritage resources. Among them, CNR was the most outstanding institution, with 42 articles published. And the University of Calabria, and University of the Basque Country also stand out.

The cooperative relationships among the institutions show close links between universities. The cooperation among the University of Pisa, University of Insubria, University of Bologna, and University of Cagliari was prominent, followed by the cooperation between University of Antwerp and University of Granada, and that between Parma University and University of Calabria.

Fig. 5
figure 5

Co-institution network on scientific analysis of murals (2011–2021)

Full size image
Fig. 6
figure 6

Co-institution density diagram on scientific analysis of murals (2011–2021)

Full size image
Table 3 Top 10 institutions on scientific analysis of murals (ranking by articles)
Full size table

Journal analysis

Under a threshold of published at least 5 documents per journal, 38 out of 276 journals were distinguished. Figure 7 presents the journal citation network with 38 nodes. According to Bradford’s law, if the number of papers published in a certain subject area within a journal is arranged in descending order, the journals in this subject area can be divided into three types: core area journals, related area journals, and non-relevant area journals [26]. The calculation formula is as follows:

$${\text{r}}_{0} = {\text{ }}2\ln \left( {{\text{e}}^{{\text{E}}} Y} \right)$$

where r0 is an estimate of how many journals should be considered to be core in a given area, E is the Euler-Mascheroni constant (0.5772) [27], and Y is the number of papers in the largest journal of the field. In this study, Y = 40. Through calculation, r0 can be calculated to be approximate to 8.532 and rounded to be 9. This means that at least nine journals should be considered core journals in the field of scientific analysis of murals, namely Journal of Cultural Heritage, Journal of Raman Spectroscopy, Microchemical Journal, Journal of Archaeological Science: Reports, Studies in Conservation, Heritage Science, Archaeometry, Archaeological and Anthropological Sciences, Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy

According to Table 4, Journal of Raman Spectroscopy ranks top in citations and ranks second in the number of articles. This meant that Raman spectroscopy is by large, the most important and used technique in cultural heritage analysis. Journal of Cultural Heritage ranks top by the number of articles and ranked third in citations. Therefore, they are currently the main journals publishing the scientific analysis of mural paintings.

According to the analysis results derived from the VOSviewer software, the scientific analysis of murals spans multiple disciplines, such as chemistry, archaeology, spectroscopy, material science, engineering, and geological science. Among them, chemistry, materials science, and archaeology are the three disciplines with the largest amount of literature, indicating that the scientific analysis of wall paintings is an extensive and in-depth study concerning multiple disciplines.

Fig. 7
figure 7

Network visualization map of citation analysis of journals publishing on scientific analysis of murals (2011–2021)

Full size image
Table 4 Nine journals in the core area of scientific analysis of murals (ranking by articles)
Full size table

Keyword co-occurrence and keyword cluster analysis

Keywords represent the core content of the literature, and high-frequency keywords effectively reflect the research hotspots in the field. As shown in Figs. 8 and 9, each keyword is represented by a node sized proportionally to its frequency. A larger number of links indicates more frequent keyword co-occurrences. The thickness of the connection reflects the strength of the connection.

Table 5 lists the top 30 high-frequency keywords. ‘pigment’ (254) appears most frequently, followed by ‘wall painting’ (244), indicating that pigment is the most important research object in this field. ‘Raman spectroscopy’, ‘spectroscopy’, ‘FT-IR’, and other keywords related to spectroscopy rank in the top 15, reflecting that spectroscopy is the most commonly used analytical method. Other high-frequency keywords, including ‘biodeterioration’, ‘degradation’, and ‘deterioration’, are related to mural biological damages and aging issues. Keywords like ‘minerals’ and ‘mortar’ are related to the component materials of wall paintings.

Table 5 The top 30 keywords of scientific analysis of murals (ranking by frequency)
Full size table

The VOSviewer software can categorize the scattered keywords in the co-occurrence network and cluster the keywords with relatively high co-occurrence frequency. In bibliometrics, a cluster in the co-occurrence network map often represents the research theme and focus [28].

As shown in Fig. 8, the keywords are divided into 8 clusters. Each cluster or a combination of clusters represented a subfield of scientific analysis of wall paintings. The keywords in the C1 (red) cluster such as ‘conservation’, ‘calcium hydroxide nanoparticles’, ‘restoration’, ‘cleaning’, and ‘construction’, ‘preventive conservation’ in the C7 (orange) represent the research of mural conservation materials and technology. At present, scientific analysis plays an important role in the whole process of mural paintings conservation. Rosina et al. applied XRD, nuclear magnetic resonance (NMR), and IR thermography (IRT) scanning to explore the soluble salts and their transport phenomena on wall paintings [29]. Ranalli et al. assessed the cleaning effects of a new agar-gauze biogel system on mural paintings by Py-GC/MS and FT-IR [30]. Researchers comprehensively applied XRD, FTIR, scanning electron microscopy coupled with energy dispersive X-ray spectrometry (SEM-EDS) etc. to analyze the effects of calcium hydroxide nanoparticle dispersions for consolidating lime mortars [31,32,33]. Su et al. used FT-IR, differential scanning calorimetry (DSC), gel permeation chromatography (GPC), and SEM to evaluate the physicochemical properties of conservation materials on wall paintings [34].

The keywords ‘biodeterioration’, ‘bacteria’, ‘fungi’, ‘microorganisms’, in the C4 (yellow) cluster represent the application of scientific analysis of biodeterioration in wall paintings. Researchers applied RS, XRF, XRD, SEM to revealed the biodegradation processes on wall paintings [35]. Optical microscopy (OM) and SEM-EDS were used to study the effect of fungal hyphae growth on the cracking, flaking, and lack of cohesion of the paint layers and mortars underneath [36]. Raman spectroscopy was used to explore the effects of microbial contamination on the color alteration of mural pigments [37].

In the C2 (green) cluster, the keywords such as ‘roman wall paintings’, ‘rock art’, ‘cave’, ‘portable XRF’, ‘prehistoric paintings’, ‘SEM’, ‘Egyptian blue’, combined with ‘Pompeii’, ‘in-situ’, ‘hyperspectral imaging’, ‘microscopy’, ‘portable Raman’ in the C6 (pink) cluster, highlighted the importance of using analysis at micro scale especially in rock art analysis but also performing in-situ analysis. And the use of portable instrumentation (mainly RS and XRF) is due to the sampling prohibition in Pompeii from the last decade. The relevant research in this area is mainly focused on the non-invasive methods and microscopic identification in t he ancient rock painting art. Researchers have adopted a multi-technique approach (OM, SEM-EDS, RS and FT-IR), aiming at a better understanding of the rock paint stratigraphy, composition, and provenance. Portable-XRF has been applied to non-invasive analyses to investigate the rock art pigments. And hand-held RS assisted with hand-held XRF were selected as the in-situ spectroscopic techniques to explore the compositions in the wall paintings [38,39,40,41,42,43,44] .

The ‘pigment’, ‘color’, ‘painting technique’, ‘binding media’, ‘GC-MS’ in the C3(blue) cluster, and ‘plaster’, ‘XRD’, ‘buildings’, ‘pigment identification’ in the C5 (purple) cluster, alongside with ‘media’, ‘layers’, ‘gilding’ in the C8 cluster (brown) were focused on the materials and techniques in the making of wall painting. Researchers applied SEM-EDS, Py-GC/MS, LC-ESI-MS, and cross-section analysis to explore the distribution of organic materials in different gilding layers and the gilding techniques. Techniques such as FT-IR, SEM-EDS, XRD, were adopted to analyze the material composition and explore the painting technique. RS, SEM-EDS, GC-MS were used to acquire information on the artistic materials and the painting technique prior to restoration [45,46,47,48,49,50].

The visualization shown in Fig. 9 can be expanded into the overlay visualization to illustrate the evolution of mural scientific analysis over time. Keywords from the period before 2017 include ‘pigment identification’, ‘in-situ’, ‘SEM-EDS’, ‘FT-Raman’. The wall painting research at that period was somewhat foundational and focused mainly on wall painting production materials and processes. After 2017, keywords such as ‘principal component analysis’, ‘mass-spectrometry’, ‘Ca(OH)2 nanoparticles’, and ‘restoration’ indicate that the development trend then was to introduce more cutting-edge analyticaland data processing methods. On the other hand, the scientific analysis was expanded to guide the research and development of mural conservation materials.

Fig. 8
figure 8

Co-occurrence network map of keywords from articles published on scientific analysis of murals (2011–2021)

Full size image
Fig. 9
figure 9

Evolution of scientific analysis of murals (2011–2021) based on the keywords

Full size image

Analysis of frequently cited literature

Ranking the articles by citations is a classic bibliometric method to reveal the most influential articles in the field [51]. Table 6 shows the top 10 frequently cited articles in mural scientific analysis from 2011 to 2021.The most frequently cited article was titled Hydroxide nanoparticles for cultural heritage: Consolidation and protection of wall paintings and carbonate materials, with a total number of 132 citations. This article provided an overview on the synthesis and preparation of colloidal systems tailored to the consolidation of wall paintings, and presented 2 case studies which are representative of typical consolidation problems [52]. The second most frequently cited article was entitled A multi-technique characterization and provenance study of the pigments used in San rock art, South Africa, with 73 citations. This study served as a starting point for the further physical analysis of southern African paints, and demonstrated the importance of using different and complimentary analytical techniques in studying the composition of wall paintings [38].

Table 6 The top 10 most frequently cited articles on scientific analysis of murals
Full size table

Conclusions

Bibliometric analysis results showed a growing number of researchers entering the field of wall painting scientific analysis, which generated a wealth of research views. Italy, Spain, China, the US, and France ranked the highest in the number of published articles in this field. There was relatively extensive cooperation among the top 10 countries of relevant publications. The co-cited journal network showed that the wall painting scientific analysis involved multiple disciplines, such as materials science, engineering, geology, and archaeology, which facilitated analysis in different ways, making the research more extensive and in-depth. Keyword clustering and co-occurrence networks showed that the research hotspots of mural scientific analysis mainly included evaluating the effect and mechanism of conservation materials and technologies, as well as the study of pigment composition, distribution, origin, and deterioration mechanism. In addition, future research could focus on innovations in conservation materials, and the application of novel technology and data analysis methods.

Data availability

All data analyzed in this study are included in the article.

References

  1. Peng W, Yu J. History of mural: material, technique and application. Public Art. 2015;2:42–8.

    Google Scholar 

  2. Yang W, Guo H. Review on the conservation of tomb murals in China. Sciences of Conservation and Archaeology. 2017;29(4):109–14.

    Google Scholar 

  3. Rees-Jones Stephen G. Early experiments in pigment analysis. Stud. Conserv. 1990;35(2):93–101.

    Google Scholar 

  4. Davy SH. LXIII Some experiments and observations on the colours used in painting by the ancients.Philos. Mag. 1815;45(205):349–59.

    Article  Google Scholar 

  5. Perez-Alonso M, Castro K, Madariaga JM. Vibrational spectroscopic techniques for the analysis of artefacts with historical, artistic and archaeological value. Curr. Anal. Chem. 2006;2(1):89–100.

    Article  CAS  Google Scholar 

  6. Guineau B. Microanalysis of painted manuscripts and of coloured archeological materials by Raman laser microprobe. J. Forensic. Sci. 1984;29(2):471–85.

    Article  CAS  Google Scholar 

  7. Guineau B. Analyse non destructive des pigments par microsonde Raman laser: exemples de l’azurite et de la malachite. Stud. Conserv. 1984;29(1):35–41.

    CAS  Google Scholar 

  8. Vandenabeele P, Edwards H, Moens L. A decade of Raman spectroscopy in art and archaeology. Chem. Rev. 2007;107(3):675–86.

    Article  CAS  Google Scholar 

  9. Carbo MTD, Reig FB, Martinez VP, Adelantado JVG. Analytical study of wall paintings of the Basilica de La Virgen de Los Desamparados de Valencia with FT-IR spectroscopy. Quim. Anal. 1997;16(4):273–9.

    Google Scholar 

  10. Andrikopoulos KS, Daniilia SX, Roussel B, Janssens K. In vitro validation of a mobile Raman-XRF micro-analytical instrument’s capabilities on the diagnosis of Byzantine icons. J. Raman Spectrosc. 2006;37(10):1026–34.

    Article  CAS  Google Scholar 

  11. Weber J, Prochaska W, Zimmermann N. Microscopic techniques to study Roman renders and mural paintings from various sites. Mater Charact. 2009;60(7):586–93.

    Article  CAS  Google Scholar 

  12. de Fonjaudran CM, Nevin A, Pique F, Cather S. Stratigraphic analysis of organic materials in wall painting samples using micro-FTIR attenuated total reflectance and a novel sample preparation technique. Anal. Bioanal. Chem. 2008;392(1–2):77–86.

    Article  CAS  Google Scholar 

  13. Gautier G, Colombini MP. GC-MS identification of proteins in wall painting samples: a fast clean-up procedure to remove copper-based pigment interferences. Talanta. 2007;73(1):95–102.

    Article  CAS  Google Scholar 

  14. Giorgi R, Baglioni M, Berti D, Baglioni P. New Methodologies for the conservation of cultural heritage: micellar solutions, microemulsions, and hydroxide nanoparticles. Accounts. Chem. Res. 2010;43(6):695–704.   

    Article  CAS  Google Scholar 

  15. Borg B, Dunn M, Ang A, Villis C. The application of state-of-the-art technologies to support artwork conservation: literature review. J. Cultural. Herit. 2020;44:239–59.

    Article  Google Scholar 

  16. Lei, Y., Wen, M., & Cheng, X. An investigation of the murals preserved in the temples under Chieftain Lu in Liancheng, Yongdeng county, Gansu province. Palace Museum Journal. 2012;(02):133–154+163. https://doi.org/10.16319/j.cnki.0452-7402.2012.02.004.

  17. Gebremariam KF, Kvittingen L, Nicholson DG. Multi-analytical investigation into painting materials and techniques: the wall paintings of Abuna Yemata Guh church. Herit. Sci. 2016;4(1):32.

    Article  CAS  Google Scholar 

  18. Du J, Zhu Z, Yang J, Wang J, Jiang X. A comparative study on the extraction effects of common agents on collagen-based binders in mural paintings. Herit. Sci. 2021;9(1):45.

    Article  CAS  Google Scholar 

  19. Liu L, He J, Ye M, Zhu Z, Zhong Q, Yang J. Spectral Characterization of Pigment from the No. 1 Cave Kizil Cave-Temple Complex. J. Spectrosc. 2019;2019:1–9.

    Google Scholar 

  20. Irazola M, Olivares M, Castro K, Maguregui M, Martinez-Arkarazo I, Manuel Madariaga J. In situ Raman spectroscopy analysis combined with Raman and SEM-EDS imaging to assess the conservation state of 16th century wall paintings. J. Raman Spectrosc. 2012;43(11):1676–84.

    Article  CAS  Google Scholar 

  21. Gao K. Research on the application of bibliometric analysis software VOSviewer. Journal of Library and Information Science. 2015;25(12):95–8.

    Google Scholar 

  22. Qiu J. Bibliometrics. Beijing: Scientific and technical documentation press; 1988.

    Google Scholar 

  23. Peng W, Zhao X, Zhao S. A Comparative study of entrepreneurial failure at home and abroad based on bibliometric analysis. R&D Management. 2019;31(4):139–50.

    Google Scholar 

  24. Garfield E. Citation analysis as a tool in journal evaluation. Science (New York). 1972;178(4060):471–9.

    Article  CAS  Google Scholar 

  25. Zupic I, Cater T. Bibliometric Methods in Management and Organization. Organ. Res. Methods. 2015;18(3):429–72. 

    Article  Google Scholar 

  26. Wang Z, Li B. Dynamic analysis of information science research focus of the past five years in China: based on Bradford law partition theory. Information and Documentation Services. 2016;03:34–40.

    Google Scholar 

  27. Alexanderson GL. Gamma: exploring Euler’s constant. Math. Intell 2005;27(1):86–8.

    Article  Google Scholar 

  28. Liu X, Wang X. Measurement of public policy influence on academia-citation analysis on the centreal committee’s document No.1 2004–2017. Journal of Intelligence. 2019;08:165–71.

    Google Scholar 

  29. Rosina E, Sansonetti A, Erba S. Focus on soluble salts transport phenomena: the study cases of Leonardo mural paintings at Sala delle Asse (Milan). Constr. Build Mater. 2017;136:643–52. 

    Article  CAS  Google Scholar 

  30. Ranalli G, Zanardini E, Rampazzi L, Corti C, Andreotti A, Colombini MP, Bosch-Roig P, Lustrato G, Giantomassi C, Zari D, Virilli P. Onsite advanced biocleaning system for historical wall paintings using new agar-gauze bacteria gel. J. Appl. Microbiol. 2019;126(6):1785–96. 

    Article  CAS  Google Scholar 

  31. Arizzi A, Gomez-Villalba LS, Lopez-Arce P, Cultrone G, Fort R. Lime mortar consolidation with nanostructured calcium hydroxide dispersions: the efficacy of different consolidating products for heritage conservation. Eur. J. Mineral. 2015;27(3):311–23.  

    Article  CAS  Google Scholar 

  32. Borsoi G, Lubelli B, van Hees R, Veiga R, Silva AS. Understanding the transport of nanolime consolidants within Maastricht limestone. J. Cult. Herit. 2016;18:242–9.

    Article  Google Scholar 

  33. Zhu J, Li X, Zhang Y, Wang J, Cao Y, Camaiti M, Wei B. Dual functionalities of Few-Layered Boron Nitrides in the design and implementation of Ca(OH)2 nanomaterials toward an efficient wall painting fireproofing and consolidation. ACS Appl. Mater. Inter. 2019;11(12):11792–9. 

    Article  CAS  Google Scholar 

  34. Su B, Zhang H, Zhang B, Jiang D, Zhang R, Tan X. A scientific investigation of five polymeric materials used in the conservation of murals in Dunhuang Mogao Grottoes. J. Cult. Herit. 2018;31:105–11.

    Article  Google Scholar 

  35. Veneranda M, Prieto-Taboada N, de Vallejuelo SFO, Maguregui M, Morillas H, Marcaida I, Castro K, Madariaga JM, Osanna M. Biodeterioration of Pompeian mural paintings: fungal colonization favoured by the presence of volcanic material residues. Environ. Sci. Pollut. R. 2017;24(24):19599–608. 

    Article  CAS  Google Scholar 

  36. Gil M, Martins MR, Carvalho ML, Souto C, Longelin S, Cardoso A, Mirao J, Candeias AE. Microscopy and microanalysis of an extreme case of salt and biodegradation in 17th century wall paintings. Microsc. Microanal. 2015;21(3):606–16.

    Article  CAS  Google Scholar 

  37. Rosado T, Fale A, Gil M, Mirao J, Candeias A, Caldeira AT. Understanding the influence of microbial contamination on colour alteration of pigments used in wall paintings—The case of red and yellow ochres and ultramarine blue. Color. Res. Appl. 2019;44(5):783–9.

    Article  Google Scholar 

  38. Bonneau A, Pearce DG, Pollard AM. A multi-technique characterization and provenance study of the pigments used in San rock art, South Africa. J. Archaeol. Sci. 2012;39(2):287–94.

    Article  CAS  Google Scholar 

  39. Mauran G, Lebon M, Detroit F, Caron B, Nankela A, Pleurdeau D, Bahain JJ. First in situ pXRF analyses of rock paintings in Erongo, Namibia: results, current limits, and prospects. Archaeol. Anthrop. Sci. 2019;11(8):4123–45.

    Article  Google Scholar 

  40. Maguregui M, Knuutinen U, Martinez-Arkarazo I, Giakoumaki A, Castro K, Madariaga JM. Field Raman analysis to diagnose the conservation state of excavated walls and wall paintings in the archaeological site of Pompeii (Italy). J. Raman. Spectrosc. 2012;43(11):1747–53.

    Article  CAS  Google Scholar 

  41. Moretto LM, Orsega EF, Mazzocchin GA. Spectroscopic methods for the analysis of celadonite and glauconite in Roman green wall paintings. J. Cult. Herit. 2011;12(4):384–91.

    Article  Google Scholar 

  42. Izzo F, Arizzi A, Cappelletti P, Cultrone G, De Bonis A, Germinario C, Graziano SF, Grifa C, Guarino V, Mercurio M, Morra V, Langella A. The art of building in the Roman period (89 BC-79 AD): mortars, plasters and mosaic floors from ancient Stabiae (Naples, Italy). Constr. Build. Mater. 2016;117:129–43.

    Article  CAS  Google Scholar 

  43. Amadori ML, Barcelli S, Poldi G, Ferrucci F, Andreotti A, Baraldi P, Colombini MP. Invasive and non-invasive analyses for knowledge and conservation of Roman wall paintings of the Villa of the Papyri in Herculaneum. Microchem. J. 2015;118:183–92.

    Article  CAS  Google Scholar 

  44. Manuel Madariaga J, Maguregui M, Fdez-Ortiz de Vallejuelo S, Knuutinen U, Castro K, Martinez-Arkarazo I, Giakoumaki A, Pitarch A. In situ analysis with portable Raman and ED-XRF spectrometers for the diagnosis of the formation of efflorescence on walls and wall paintings of the Insula IX 3 (Pompeii, Italy). J. Raman. Spectrosc. 2014;45(11–12):1059–67.

    Article  CAS  Google Scholar 

  45. Zhou Z, Shen L, Li C, Wang N, Chen X, Yang J, Zhang H. Investigation of gilding materials and techniques in wall paintings of Kizil Grottoes. Microchem. J. 2020;154:104548.

    Article  CAS  Google Scholar 

  46. Malletzidou L, Zorba TT, Kyranoudi M, Mastora P, Karfaridis D, Vourlias G, Pavlidou E, Paraskevopoulos KM. The dome of Rotunda in Thessaloniki: investigation of a multi-pictorial phase wall painting through analytical methods. Spectrochim. Acta. A. 2021;262:12010.

    Article  CAS  Google Scholar 

  47. Cortea IM, Ghervase L, Tentea O, Parau AC, Radvan R. First analytical study on second-century wall paintings from Ulpia Traiana Sarmizegetusa: insights on the materials and painting technique. Int. J. Archit. Herit. 2020;14(5):751–61.

    Article  Google Scholar 

  48. Tomasini EP, Carcamo J, Rodriguez DMC, Careaga V, Gutierrez S, Landa CR, Sepulveda M, Guzman F, Pereira M, Siracusano G, Maier MS. Characterization of pigments and binders in a mural painting from the Andean church of San Andres de Pachama (northernmost of Chile). Herit. Sci. 2018;6:61.

    Article  CAS  Google Scholar 

  49. Cheilakou E, Troullinos M, Koui M. Identification of pigments on Byzantine wall paintings from Crete (14th century AD) using non-invasive Fiber Optics Diffuse Reflectance Spectroscopy (FORS). J. Archaeol. Sci. 2014;41:541–55.

    Article  CAS  Google Scholar 

  50. Westlake P, Siozos P, Philippidis A, Apostolaki C, Derham B, Terlixi A, Perdikatsis V, Jones R, Anglos D. Studying pigments on painted plaster in Minoan, Roman and Early Byzantine Crete. A multi-analytical technique approach. Anal. Bioanal. Chem. 2012;402(4):1413–32.

    Article  CAS  Google Scholar 

  51. Ma X, Liu W, Chen Y. Knowledge mapping analysis of domestic research on big data. Journal of University of Electronic Science and Technology of China (Social Sciences Edition). 2018;20(3):9–15.

    Google Scholar 

  52. Chelazzi D, Poggi G, Jaidar Y, Toccafondi N, Giorgi R, Baglioni P. Hydroxide nanoparticles for cultural heritage: consolidation and protection of wall paintings and carbonate materials. J. Colloid. Interf. Sci. 2013;392:42–9.

    Article  CAS  Google Scholar 

Download references

Acknowledgements

Not applicable.

Funding

This work was supported by National Natural Science Foundation of China (Project No. 51908464), China Ministry of Education Humanities and Social Sciences Project (Project No. 18XJCZH011), the Alexander von Humboldt Foundation, Open Research Fund of Key Scientific Research Base of Conservation and Restoration for Murals as Collection and Materials Science in State Administration for Cultural Heritage, Joint International Research Laboratory of Environmental and Social Archaeology (Project No. JoInRLESA202202), XMU Undergraduate Innovation and Entrepreneurship Training Programs (Project No. S202210384629).

Author information

Authors and Affiliations

  1. Research and Practice Base of Conservation Science and Engineering, Department of History, College of Humanities, Xiamen University, Xiamen, 361005, China

    Zhanyun Zhu, Xiuya Yao, Yaling Qin & Xi Zhao

  2. Department of Archaeology, Max Planck Institute for the Science of Human History, D-07745, Jena, Germany

    Zhanyun Zhu

  3. Key Scientific Research Base of Conservation and Restoration for Murals as Collection and Materials Science in State Administration for Cultural Heritage, Shaanxi History Museum, 710061, Xi׳‬an, China

    Zhanyun Zhu & Zhiyong Lu

  4. Joint International Research Laboratory of Environmental and Social Archaeology, Institute of Cultural Heritage, Shandong University, Qingdao, 266237, China

    Zhanyun Zhu & Qinglin Ma

  5. Xiamen Academy of Arts and Design, Fuzhou University, Xiamen, 361000, China

    Liu Liu

Authors
  1. Zhanyun Zhu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xiuya Yao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yaling Qin
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Zhiyong Lu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Qinglin Ma
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Xi Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Liu Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

ZZ: conceptualization, methodology, validation, investigation, data analyses, writing - original draft, writing - review and editing, project administration. XY: methodology, validation, investigation, data analyses, writing - original draft, writing - review and editing. YQ: investigation, data analyses. ZL: writing - review and editing, project administration. QM: data analyses, project administration. XZ: writing - review and editing. LL: methodology, project administration. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Liu Liu.

Ethics declarations

Competing interests

The authors declare that they have no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhu, Z., Yao, X., Qin, Y. et al. Visualization and mapping of literature on the scientific analysis of wall paintings: a bibliometric analysis from 2011 to 2021. Herit Sci 10, 105 (2022). https://doi.org/10.1186/s40494-022-00735-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s40494-022-00735-0

Keywords