Machine learning-assisted non-destructive plasticizer identification and quantification in historical PVC objects based on IR spectroscopy
Rijavec, Tjaša ; Ribar, David LU
; Markelj, Jernej
; Strlič, Matija
and Kralj Cigić, Irena
(2022)
In Scientific Reports
12(1).
- Abstract
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... (More)
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.
(Less)
- author
- Rijavec, Tjaša
; Ribar, David
LU
; Markelj, Jernej
; Strlič, Matija
and Kralj Cigić, Irena
- publishing date
- 2022-12
- type
- Contribution to journal
- publication status
- published
- in
- Scientific Reports
- volume
- 12
- issue
- 1
- article number
- 5017
- publisher
- Nature Publishing Group
- external identifiers
-
- scopus:85126894042
- pmid:35322097
- ISSN
- 2045-2322
- DOI
- 10.1038/s41598-022-08862-1
- language
- English
- LU publication?
- no
- id
- 4fb5a29b-0728-49c8-af54-d36d35646291
- date added to LUP
- 2023-09-20 22:55:41
- date last changed
- 2024-05-17 05:02:07
@article{4fb5a29b-0728-49c8-af54-d36d35646291,
abstract = {{<p>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.</p>}},
author = {{Rijavec, Tjaša and Ribar, David and Markelj, Jernej and Strlič, Matija and Kralj Cigić, Irena}},
issn = {{2045-2322}},
language = {{eng}},
number = {{1}},
publisher = {{Nature Publishing Group}},
series = {{Scientific Reports}},
title = {{Machine learning-assisted non-destructive plasticizer identification and quantification in historical PVC objects based on IR spectroscopy}},
url = {{http://dx.doi.org/10.1038/s41598-022-08862-1}},
doi = {{10.1038/s41598-022-08862-1}},
volume = {{12}},
year = {{2022}},
}