Machine learning-assisted non-destructive plasticizer identification and quantification in historical PVC objects based on IR spectroscopy

Rijavec, Tjaša (avtor), Ribar, David (avtor), Markelj, Jernej (avtor), Strlič, Matija (avtor), Kralj Cigić, Irena (avtor)

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.

• Jezik: Angleški jezik
• Ključne besede: machine learning, plasticizer, PVC, IR spectroscopy, historical objects, analytical chemistry, computational methods, infrared spectroscopy, polymer chemistry
• Vrsta gradiva: Članek v reviji
• Tipologija: 1.01 - Izvirni znanstveni članek
• Organizacija: FKKT - Fakulteta za kemijo in kemijsko tehnologijo
• Status publikacije: Objavljeno
• Različica publikacije: Objavljena publikacija
• Leto izida: 2022
• Št. strani: 11 str.
• Številčenje: Vol. 12, art. 5017
• PID: 20.500.12556/RUL-145267
• UDK: 543.42:004.85
• ISSN pri članku: 2045-2322
• DOI: 10.1038/s41598-022-08862-1
• COBISS.SI-ID: 102113283
• Datum objave v RUL: 14.04.2023

RIJAVEC, Tjaša, RIBAR, David, MARKELJ, Jernej, STRLIČ, Matija in KRALJ CIGIĆ, Irena, 2022, Machine learning-assisted non-destructive plasticizer identification and quantification in historical PVC objects based on IR spectroscopy. Scientific reports [na spletu]. 2022. Vol. 12,  5017. [Dostopano 7 april 2025]. DOI 10.1038/s41598-022-08862-1. Pridobljeno s: https://repozitorij.uni-lj.si/IzpisGradiva.php?lang=slv&id=145267

Gradivo je del revije

• Naslov: Scientific reports
• Skrajšan naslov: Sci. rep.
• Založnik: Nature Publishing Group
• ISSN: 2045-2322
• COBISS.SI-ID: 18727432

Licence

• Licenca: CC BY 4.0, Creative Commons Priznanje avtorstva 4.0 Mednarodna
• Povezava: http://creativecommons.org/licenses/by/4.0/deed.sl
• Opis: To je standardna licenca Creative Commons, ki daje uporabnikom največ možnosti za nadaljnjo uporabo dela, pri čemer morajo navesti avtorja.

Sekundarni jezik

• Jezik: Slovenski jezik
• Ključne besede: strojno učenje, mehčalo, PVC, IR spektroskopija, zgodovinski predmeti