Contract number



Department of Biology

Type of project

ARRS projects

Type of project

Basic research project




01.07.2019 - 30.06.2023


1.30 FTE

Project manager at BF

Zalar Polona


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.


Detailed descriptions of working packages  and timing of experiments:

WP1: Analysis of mouldy paintings: materials, damages, moulds

Duration: 1st – 18th month; Leader: Polona Zalar

Participants: BF, ZVKDS

Objective: Insight into mycoflora contaminating canvas paintings and materials of infected as well as non-infected parts of the selected paintings

Task 1.1: Compilation of data on restored oil paintings on canvas from the last two decades. More than 100 paintings have been renovated following approximately the same analytical standards in the past two decades at ZVKDS. Data on their origin, storage, material and damage analyses, potential fungal contamination, and used restoration materials will be pooled together. Accessible paintings will be checked for visible fungal growth with the emphasis on the restored parts, by naked eye and stereomicroscopic techniques.

Task 1.2: Selection and description of damaged canvas paintings for restoration: Paintings exhibited in different Slovene institutions (museums, religious institutions) will be examined by naked eye and stereomicroscopic techniques. The paintings which will show undoubted infestation by fungi will be selected for further analyses. If data will be available, the history of the paintings will be investigated.

Task 1.3: Identification of materials composing selected paintings: Analyses will be carried out non-invasively, directly on the investigated object, and on extracted microsamples of paintings. Paintings will be sampled on contaminated areas as well as on unaffected parts. Material composition of all layers composing the painting will be studied by different microscopic, spectroscopic, and immunochemical techniques to identify all constituent materials and possible deterioration products.

Task 1.4: Isolation and identification of fungi on selected paintings: Paintings will be subjected to mycological analysis using culture-dependent techniques: Using ESwab (Copan) samples will be taken from contaminated and from non-contaminated parts, and will be subsequently plated on selected culture media. Isolated cultures will be preserved in Ex microbial culture collection. The identification of cultures will base on macro- and micro-morphological characterization, and will be supplemented with the DNA sequencing and bioinformatic analyses of molecular barcodes selected according to the fungal genus.

Task 1.5: Detection of fungi based on total extracted DNA from paintings: Metabarcoding analysis of amplified ITS2 region from total DNA isolated from sampling fluid obtained by swabbing will be performed. Active and non-active fungi will be distinguished by pretreatment of samples with propidium monoazide (PMA). NGS technology will be applied.


  • D1.1 List of restored paintings and all the collected data (ZVKDS)
  • D1.2: Selection of 10 mouldy oil paintings on canvas (ZVKDS, BF)
  • D1.3: List of materials in individual layers of each sample from selected paintings and the description of their state of preservation (ZVKDS)
  • D1.4: The list of fungal isolates from paintings preserved in Ex culture collection (BF)
  • D1.5: The list of fungal OTU’s collected by metabarcoding analyses (BF)


  • M1.1: Compiled database of restored paintings (12th month)
  • M1.2: Selected 10 mouldy paintings for further analyses and restauration procedures (2nd month)
  • M1.3: Identified materials and damages of paintings (18th month)
  • M1.4: Detected mycobiomes of paintings by culture-dependent and culture-independent techniques (18th month)

WP2: Data analysis with machine learning methods

Duration: 7 – 30th month; Leader: Sašo Džeroski

Participants: IJS

Objective: Provide insight into the relations between different influencing factors (materials, age, storage conditions of paintings) and the moulding of paintings, as well as subsequent damages.

Task 2.1: Analysis of data on damaged & restored paintings, collected at ZVKDS in the last 2 decades: Using machine learning methods for multi-target prediction, such as ensembles of predictive clustering trees, a large dataset (N=>100) obtained from the register of restored paintings of ZVKDS, pooled together as D 1.1, will be analysed. Predictive models relating influencing factors, such as materials, age, and storage conditions of paintings and the presence/ type of damage will be built. The most important factors causing moulding will be identified by ensemble-based feature ranking.

Task 2.2: Analysis of data related to damaged paintings newly selected for restoration: Machine learning methods will be used on a complex set of newly obtained data related to selected mouldy paintings (D 1.2) prior to their restoration (N=10). The composition of the fungal community assessed by culture-dependent and culture-independent techniques from samples taken from the paintings will be related to influencing factors, e.g. materials.


  • D2.1: Predictive models for damage, learned from existing data on damaged/restored paintings
  • D2.2: Predictive models for fungal community composition, learned from newly obtained data


  • M1.1: Relative importance scores for factors affecting moulding of paintings (15th month)
  • M1.2: Relative importance scores for factors affecting infestation/moulding of paintings with particular fungal species (24th month)

WP3: Planning the conservation-restoration procedure

Duration: 18 – 20th month; Leader: Barbka Gosar Hirci

Participants: ZVKDS

Objective: To plan the conservation-restoration procedure for each of the extensively sampled paintings in order to test the selected materials in further steps of the project (WP4) on selected fungi.

Task 3.1: Selection of the methods and materials for conservation-restoration processes: Theoretical planning of the conservation-restoration processes will help us when planning "mock-up" samples. The methods and materials will be selected individually for each of the analysed paintings, according to their deterioration and the environmental conditions.

Deliverable: D3.1 The list of materials and conservation-restoration processes

Milestone: M3.1: Plan of conservation-restoration processes and materials for each of the analysed paintings (20th month)

WP4: Preparation, inoculation and analysis of laboratory samples

Duration: 24 – 28th month; Leader: Katja Kavkler

Participants: ZVKDS, BF

Objective: Because of the restricted sample size of artworks for analyses laboratory samples provide a useful compromise to study colonisation of selected materials with selected fungi, as well as more detailed analyses of damages.

Task 4.1: Preparation and inoculation of "mock-up" (laboratory) samples: Laboratory samples will be prepared according to the analyses from WP1 and data analyses from WP2. Traditional technology will be applied as well as typical conservation-restoration materials. A part of the samples will be artificially aged. Based on the predictive models for fungal communities (D2.2), we will select the key fungal species contaminating canvas paintings with the above materials. Laboratory samples will be inoculated by a defined suspensions of fungal spores (sporulating fungi) or fragmented mycelium (non-sporulating fungi) and incubated in a defined moisture and temperature conditions for 1 -4 weeks. 

Task 4.2: Analysis of paint materials after inoculation with different fungal species: After inoculation raw materials used for laboratory paintings as well as those from inoculated paintings will be analysed. We will especially focus on deterioration products and other types of changes in selected materials using same or similar approaches as in Task 1.3.

Task 4.3: Optimization of Q-PCR for quantification of fungal biomass: laboratory samples will be used to implement the methodology for quantification of colonization by Q-PCR based on the presence of beta-actin gene. This methodology will serve to allow the comparison of infection rates of different materials with different fungi. The optimized methodology will be used on DNA extracted from original painting samples.


  • D4.1: Plan for preparation of laboratory samples (ZVKDS)
  • D4.2: Inoculation of laboratory samples with selected fungi (BF)
  • D4.3: List of changes on laboratory samples caused by fungi (ZVKDS)
  • D4.4: Optimized Q-PCR on laboratory samples.


  • M4.1: Prepared laboratory samples (24th month)
  • M4.2: Results on analysis of inoculated laboratory samples (33th month)
  • M4.3: Quantification of fungal infestation on laboratory samples and original paintings (36th month).

WP5: Data analyses and recommendations

Duration: 18 – 24th month; Leader: Polona Zalar

Participants: IJS, BF, ZVKDS

Objective: To gather, present and critically review all the results by all participants.

Task: 5.1: A list of guidelines concerning the treatment of mouldy paintings, useful for restores.


  • D5.1: Determination of critical risk points (materials, storage conditions) for paintings on canvas stored in churches and/or museums for contaminations with fungi (ZVKDS, BF)
  • D5.2: A list of fungal species, ranked by the risk level of such contamination for the long-time integrity of paintings (BF, ZVKDS)
  • D5.3: A list of degradation changes caused by fungi on different materials (ZVKDS)
  • D5.4: A list of materials used in painting conservation/restoration tested for most problematic fungi (BF, ZVKDS)

Milestone: M5.1 Joint list of guidelines for analysis and restoration of mouldy paintings (36th month)