20.500.12556/RUL-138235 A multidisciplinary study of biodeteriorated Celje Ceiling, a tempera painting on canvas Interdisciplinary investigations of damaged cultural heritage objects have nowadays become standard practise. Numerous techniques in various fields may generate large amounts of data, difficult to interpret. Machine learning was applied to data collected from samples of a painting to build a predictive model of potential further biodeterioration and consequent damage to the paintings. We used this strategy for a deteriorated 17th century Celje Ceiling, tempera painting in a wooden frame, covering 143 m2 of the ceiling. An interdisciplinary approach, from monitoring microclimatic conditions and wood moisture content, to examinations using UV and VIS photography, analyses of paint layers and decay products by Raman and FTIR spectroscopy, SEM-EDS, and protein binders by ELISA, was used to assess current condition of the painting. Data from 535 painting's samples were subjected to analysis by machine learning methods. Moulding, the main cause of biodeterioration, was found to be strongly influenced by the position of the painting in the room and related microclimatic conditions. The presence of a coating layer, protein binders and pigments, such as goethite, ultramarine and kaolinite, had important role in mould development as well. The painting was mainly damaged by currently non-active, presumably xerophilic Aspergillus species, as determined by microscopy, cultivation and amplicon sequencing tempera painting wood environmental monitoring amplicon sequencing fungi bacteria les tempera slikanje spremljanje stanja okolja sekvenciranje amplikonov glive bakterije true false true Angleški jezik Slovenski jezik Članek v reviji 2022-07-13 07:29:31 2022-07-13 07:29:38 2022-09-15 04:16:25 0000-00-00 00:00:00 2022 0 0 1 spletni vir (1 datoteka PDF ([14 str.])) Vol. 170 2022 0000-00-00 Zaloznikova Objavljeno NiDoloceno 0000-00-00 0000-00-00 2022-02-28 630*8 1879-0208 10.1016/j.ibiod.2022.105389 99197187 https://www.sciencedirect.com/science/article/pii/S0964830522000178 1 8fb6db93-026c-11ed-8aca-00155dcfd717 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=158465 RAZ_Kavkler_Katja_2022.pdf RAZ_Kavkler_Katja_2022.pdf 1 7991580FC9E46100EA16BE844A9EBE18 7eae2bbeb9337388f124d07aa899b7bae0d6e7e716eba927eb2b1470e8fab5ed fbfa7d7c-026c-11ed-8aca-00155dcfd717 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=158466 Biotehniška fakulteta 0 0 0