20.500.12556/RUL-145267 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. machine learning plasticizer PVC IR spectroscopy historical objects analytical chemistry computational methods infrared spectroscopy polymer chemistry strojno učenje mehčalo PVC IR spektroskopija zgodovinski predmeti true false true Angleški jezik Slovenski jezik Članek v reviji 2023-04-14 12:06:50 2023-04-14 12:06:51 2023-05-24 15:53:59 0000-00-00 00:00:00 2022 0 0 11 str. Vol. 12, art. 5017 23 Mar. 2022 0000-00-00 Zaloznikova Objavljeno NiDoloceno 0000-00-00 0000-00-00 0000-00-00 543.42:004.85 2045-2322 10.1038/s41598-022-08862-1 102113283 s41598-022-08862-1.pdf s41598-022-08862-1.pdf 1 D979A9B74EF3F89AAE5F494195418125 c5b846a056f9f1523b728b523f20dd6919b53f94a86c23cb7c164fd6c8f0110d 2656a7af-daac-11ed-a314-00155dcfd717 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=168017 https://www.nature.com/articles/s41598-022-08862-1 1 ed8bb88c-daab-11ed-a314-00155dcfd717 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=168016 Fakulteta za kemijo in kemijsko tehnologijo 0 0 0