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Projects / Programmes source: ARIS

Restoration of moldy canvas paintings: improvement or deterioration?

Research activity

Code Science Field Subfield
2.14.00  Engineering sciences and technologies  Textile and leather   
1.03.00  Natural sciences and mathematics  Biology   

Code Science Field
B230  Biomedical sciences  Microbiology, bacteriology, virology, mycology 

Code Science Field
2.05  Engineering and Technology  Materials engineering 
1.06  Natural Sciences  Biological sciences 
Keywords
cultural heritage, textile, damages, fungi, interdisciplinary research, microbiology, chemistry, computer sciences
Evaluation (rules)
source: COBISS
Researchers (23)
no. Code Name and surname Research area Role Period No. of publicationsNo. of publications
1.  36302  Petra Bešlagić  Interdisciplinary research  Researcher  2019 - 2023  251 
2.  36220  PhD Martin Breskvar  Computer science and informatics  Researcher  2019 - 2023  35 
3.  36374  PhD Jerneja Čremožnik Zupančič  Microbiology and immunology  Researcher  2020 - 2023  67 
4.  11130  PhD Sašo Džeroski  Computer science and informatics  Researcher  2019 - 2023  1,197 
5.  56599  Mitja Gajšek  Microbiology and immunology  Researcher  2022 - 2023 
6.  33707  Barbka Gosar Hirci  Interdisciplinary research  Researcher  2019 - 2023  214 
7.  25974  PhD Cene Gostinčar  Biotechnology  Researcher  2019 - 2023  329 
8.  05935  PhD Nina Gunde-Cimerman  Biotechnology  Researcher  2019 - 2023  1,255 
9.  31457  PhD Katja Kavkler  Textile and leather  Researcher  2019 - 2023  488 
10.  37937  PhD Tilen Knaflič  Physics  Researcher  2021 - 2023  20 
11.  31050  PhD Dragi Kocev  Computer science and informatics  Researcher  2019 - 2023  203 
12.  36373  PhD Monika Kos  Biology  Researcher  2020 - 2023  67 
13.  53699  Amela Kujović  Biochemistry and molecular biology  Junior researcher  2023  24 
14.  35055  Lea Legan  Chemistry  Researcher  2019 - 2023  158 
15.  34810  Mojca Matul    Technical associate  2019 - 2021  47 
16.  34266  PhD Monika Novak Babič  Medical sciences  Researcher  2019 - 2020  152 
17.  35834  PhD Klara Retko  Chemistry  Researcher  2019 - 2023  149 
18.  28079  PhD Polonca Ropret  Chemistry  Researcher  2019 - 2023  286 
19.  34452  PhD Nikola Simidjievski  Computer science and informatics  Researcher  2019 - 2023  58 
20.  18510  PhD Martina Turk  Biochemistry and molecular biology  Researcher  2019 - 2023  190 
21.  34662  PhD Vedrana Vidulin  Computer science and informatics  Beginner researcher  2019 - 2020  56 
22.  16103  PhD Polona Zalar  Microbiology and immunology  Head  2019 - 2023  462 
23.  56298  Luen Zidar  Biotechnology  Researcher  2021 - 2023 
Organisations (3)
no. Code Research organisation City Registration number No. of publicationsNo. of publications
1.  0106  Jožef Stefan Institute  Ljubljana  5051606000  90,038 
2.  0481  University of Ljubljana, Biotechnical Faculty  Ljubljana  1626914  66,599 
3.  2316  Institute for the protection of Cultural Heritage of Slovenia  Ljubljana  1423215  3,780 
Abstract
Mouldy art paintings on textile canvas are routinely encountered during conservation-restoration practices. Some paintings even become overgrown by fungi relatively soon after being subject to conservation-restoration intervention, if they are returned to the environment with unchanged micro-climatic conditions. Unfavourable conditions are especially encountered in churches or other sacral buildings, where due to unregulated temperature and air humidity conditions occasionally become favourable for fungal growth. Accordingly, such cases encouraged us to propose the presented research not only on unrestored but also on restored paintings. Little is known about how the different canvas materials and also paint, ground and other materials, as well as storage conditions influence fungal growth, or how fungi alter theses materials. The proposed project targets in-situ identification/analysis of materials with non-invasive infrared (FTIR), Raman and X-ray fluorescence spectroscopy (XRF), and determination of actively involved fungal contaminants through culture dependent and culture independent (PCR based metabarcoding techniques) analyses. Additionally, standardized laboratory tests of selected critical materials inoculated with selected fungi at defined temperatures and relative humidity are then suggested to study their impact. Already established methodologies, individually developed by all the involved different disciplines are adopted and used on original and artificially inoculated materials. A new approach is suggested to target, beside all fungal cells that can be loaded on artifacts with dust or are already dead due to long-term contaminations, also actively growing fungi. It is based on propidium monoazide, binding to exposed DNA of inactice /dead cells, and interfering with PCR amplification. The research relating to the diversity of active fungi contaminating easel paintings will assure the elucidation of the key species, and consequently allow testing of materials for their successful use in conservation-restoration practises. In order to quantify fungal biomass, q-PCR techniques that target the single copy gene beta-actin will be applied. Well-established analytical techniques for analyses of organic and inorganic paint constituents (optical and scanning electron microscopy, FTIR and Raman spectroscopy) will be used for the identification of paint layers applied either originally or in the context of already performed conservation-restoration interventions. Enzyme-linked immunosorbent assays (ELISA) will be used for the identification of protein binders. The combination of ELISA and immunofluorescent microscopy (IFM) will be used to study the degradation and migration of proteins across paint cross sections. This approach presents a novelty in conservation-restoration science. The use of electron paramagnetic resonance (EPR) allows the study of the degradation of pigments caused by fungal metabolites, which is an original approach. A large number of obtained data will be, as not yet before, processed with machine learning methods, which will hierarchically identify the main factors influencing the choice of methods and materials needed for the restoration of damages, depending on the microclimatic conditions and the dominant fungus contaminant. In this interdisciplinary study scientists from five different fields, microbiology, (bio)chemistry, textile science, computer science, and conservation-restoration, will collaborate in order to support the conservators-restorers in the preservation of cultural heritage objects.
Significance for science
Protection of cultural heritage is a priority topic of the current research project call. Cultural heritage items can deteriorate considerably due to inadequate (micro)climatic conditions of storage and display. Paintings on canvas (easel paintings) are objects particularly sensitive to environmental influences, due to a large proportion of chemically less stable organic materials, prone to microbial degradation. In particular, paintings in churches and other sacral buildings with unregulated temperature and relative air humidity are often mouldy, before, but often also after conservation-restoration interventions, which is mainly related to the lack of knowledge about the basic fungal contaminants. Successful restoration is therefore complex and requires interdisciplinary approaches. The proposed project combines experts in different disciplines with the goal of establishing methodology in the individual contributing fields. We will merge existing data from analyses of paintings that have been restored over the past two decades and will subject them to machine learning computer approaches to gain insight into the main factors and materials that cause moulding. On a smaller number of mouldy paintings prior restoration, we will use various techniques to identify the main active fungal contaminants, which will be studied and used in further testing procedures on laboratory samples on the most susceptible materials for fungi. The intersections by extracting knowledge from a large amount of data by machine learning methods will result in the elucidation of the key parameters of contamination and lead to the construction of predictive models (to be used for recommending appropriate restoration materials and procedures). During the research, we will develop more specific workflows for the detection of active moulds and the extent of infections, as well as for identification of deterioration products in artwork materials. Determination of degradation products and influences of selected fungal species on canvas easel paintings will help conservators-restorers to understand contaminated objects and to predict, which materials can be used for conservation-restoration processes to reduce possible new contamination by fungi.
Significance for the country
Protection of cultural heritage is a priority topic of the current research project call. Cultural heritage items can deteriorate considerably due to inadequate (micro)climatic conditions of storage and display. Paintings on canvas (easel paintings) are objects particularly sensitive to environmental influences, due to a large proportion of chemically less stable organic materials, prone to microbial degradation. In particular, paintings in churches and other sacral buildings with unregulated temperature and relative air humidity are often mouldy, before, but often also after conservation-restoration interventions, which is mainly related to the lack of knowledge about the basic fungal contaminants. Successful restoration is therefore complex and requires interdisciplinary approaches. The proposed project combines experts in different disciplines with the goal of establishing methodology in the individual contributing fields. We will merge existing data from analyses of paintings that have been restored over the past two decades and will subject them to machine learning computer approaches to gain insight into the main factors and materials that cause moulding. On a smaller number of mouldy paintings prior restoration, we will use various techniques to identify the main active fungal contaminants, which will be studied and used in further testing procedures on laboratory samples on the most susceptible materials for fungi. The intersections by extracting knowledge from a large amount of data by machine learning methods will result in the elucidation of the key parameters of contamination and lead to the construction of predictive models (to be used for recommending appropriate restoration materials and procedures). During the research, we will develop more specific workflows for the detection of active moulds and the extent of infections, as well as for identification of deterioration products in artwork materials. Determination of degradation products and influences of selected fungal species on canvas easel paintings will help conservators-restorers to understand contaminated objects and to predict, which materials can be used for conservation-restoration processes to reduce possible new contamination by fungi.
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