20.500.12556/RUL-134404 Machine learning-assisted non-destructive plasticizer identification and quantification in historical PVC objects based on IR spectroscopy Non-destructive spectroscopic analysis combined with machine learning rapidly provides information on the identity and content of plasticizers in PVC objects of heritage value. For the first time, a large and diverse collection of more than 100 PVC objects in different degradation stages and of diverse chemical compositions was analysed by chromatographic and spectroscopic techniques to create a dataset used to construct classification and regression models. Accounting for this variety makes the model more robust and reliable for the analysis of objects in museum collections. Six different machine learning classification algorithms were compared to determine the algorithm with the highest classification accuracy of the most common plasticizers, based solely on the spectroscopic data. A classification model capable of the identification of di(2-ethylhexyl) phthalate, di(2-ethylhexyl) terephthalate, diisononyl phthalate, diisodecyl phthalate, a mixture of diisononyl phthalate and diisodecyl phthalate, and unplasticized PVC was constructed. Additionally, regression models for quantification of di(2-ethylhexyl) phthalate and di(2-ethylhexyl) terephthalate in PVC were built. This study of real-life objects demonstrates that classification and quantification of plasticizers in a general collection of degraded PVC objects is possible, providing valuable data to collection managers. PVC chromatography machine learning classification regression spectroscopy plasticizers heritage science preventive conservation true false false Angleški jezik Ni določen Neznano 2022-01-13 13:25:43 2022-01-13 13:40:50 2025-04-16 12:50:33 0000-00-00 00:00:00 2022 0 0 0000-00-00 NiDoloceno NiDoloceno NiDoloceno 0000-00-00 0000-00-00 0000-00-00 2022-04-30 RAZ_Rijavec_Tjasa_2022.docx RAZ_Rijavec_Tjasa_2022.docx 1 37FF1CE1BAEC09F001103496E7B2F99C f13ac7038ae234e13651ec992e5f81400c17691e72532138e953b1bfe90fa8e6 09cc29d3-748b-11ec-abdb-00155dcfd717 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=152856 RAZ_Rijavec_Tjasa_2022.zip RAZ_Rijavec_Tjasa_2022.zip 1 18AF60A6D929D59236A61A2511EED7D4 2809ed126ab533c006d4d30305a1c23d78c1fc64e1e0ed82f47da9732ff71575 ec1d6b0c-748a-11ec-abdb-00155dcfd717 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=152855 Fakulteta za kemijo in kemijsko tehnologijo 0 0 0