Prediction of polyethylene density from FTIR and Raman spectroscopy using multivariate data analysis

To contribute to the targeted 10 million tons per year of recycled plastic in Europe by 2025 and to improve the mechanical sorting degree of polyethylene (PE) products, density prediction models were developed from Fourier transform infrared-attenuated total reflectance (FTIR-ATR) and Raman spectros...

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Autores principales:	M. Bredács, C. Barretta, L.F. Castillon, A. Frank, G. Oreski, G. Pinter, S. Gergely
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	Polyethylene Density prediction Recycling FTIR-ATR and Raman spectroscopy Multivariate data analysis PCA and PLS models Polymers and polymer manufacture TP1080-1185
Acceso en línea:	https://doaj.org/article/54e7b65ebc91413eb9d8d5610b817ee0
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id	oai:doaj.org-article:54e7b65ebc91413eb9d8d5610b817ee0
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spelling	oai:doaj.org-article:54e7b65ebc91413eb9d8d5610b817ee02021-11-14T04:28:12ZPrediction of polyethylene density from FTIR and Raman spectroscopy using multivariate data analysis0142-941810.1016/j.polymertesting.2021.107406https://doaj.org/article/54e7b65ebc91413eb9d8d5610b817ee02021-12-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S0142941821003500https://doaj.org/toc/0142-9418To contribute to the targeted 10 million tons per year of recycled plastic in Europe by 2025 and to improve the mechanical sorting degree of polyethylene (PE) products, density prediction models were developed from Fourier transform infrared-attenuated total reflectance (FTIR-ATR) and Raman spectroscopic data. State-of-the-art sorting in mechanical recycling provides separated polymer classes, however an improved classification with specific chemical and physical features such as density or melt flow rate has not been developed yet.Applying multivariate data analysis (MVDA) on the spectral datasets of 10 different PE materials, one FTIR-ATR and two Raman spectra based partial least square (PLS) density models were developed. However, whereas all three models are applicable to predict PE density accurately, the Raman models have shown some advantages. Firstly, less principle components (PC) are needed and secondly the density can be assessed with higher accuracy, due to the more robust cross-validated PLS model. Moreover, the obtained PC-s indicate that in the FTIR-ATR model the CH3/CH2 ratio, while in the Raman model the CH2, CH and the crystalline C–C bands can be correlated with the PE density. The most accurate PLS model was obtained from the 1500-1000 cm−1 Raman shift region. The developed models could improve the density based mechanical separation of PE and consequently increase the quality of recycled and reprocessed PE products.M. BredácsC. BarrettaL.F. CastillonA. FrankG. OreskiG. PinterS. GergelyElsevierarticlePolyethyleneDensity predictionRecyclingFTIR-ATR and Raman spectroscopyMultivariate data analysisPCA and PLS modelsPolymers and polymer manufactureTP1080-1185ENPolymer Testing, Vol 104, Iss , Pp 107406- (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Polyethylene Density prediction Recycling FTIR-ATR and Raman spectroscopy Multivariate data analysis PCA and PLS models Polymers and polymer manufacture TP1080-1185
spellingShingle	Polyethylene Density prediction Recycling FTIR-ATR and Raman spectroscopy Multivariate data analysis PCA and PLS models Polymers and polymer manufacture TP1080-1185 M. Bredács C. Barretta L.F. Castillon A. Frank G. Oreski G. Pinter S. Gergely Prediction of polyethylene density from FTIR and Raman spectroscopy using multivariate data analysis
description	To contribute to the targeted 10 million tons per year of recycled plastic in Europe by 2025 and to improve the mechanical sorting degree of polyethylene (PE) products, density prediction models were developed from Fourier transform infrared-attenuated total reflectance (FTIR-ATR) and Raman spectroscopic data. State-of-the-art sorting in mechanical recycling provides separated polymer classes, however an improved classification with specific chemical and physical features such as density or melt flow rate has not been developed yet.Applying multivariate data analysis (MVDA) on the spectral datasets of 10 different PE materials, one FTIR-ATR and two Raman spectra based partial least square (PLS) density models were developed. However, whereas all three models are applicable to predict PE density accurately, the Raman models have shown some advantages. Firstly, less principle components (PC) are needed and secondly the density can be assessed with higher accuracy, due to the more robust cross-validated PLS model. Moreover, the obtained PC-s indicate that in the FTIR-ATR model the CH3/CH2 ratio, while in the Raman model the CH2, CH and the crystalline C–C bands can be correlated with the PE density. The most accurate PLS model was obtained from the 1500-1000 cm−1 Raman shift region. The developed models could improve the density based mechanical separation of PE and consequently increase the quality of recycled and reprocessed PE products.
format	article
author	M. Bredács C. Barretta L.F. Castillon A. Frank G. Oreski G. Pinter S. Gergely
author_facet	M. Bredács C. Barretta L.F. Castillon A. Frank G. Oreski G. Pinter S. Gergely
author_sort	M. Bredács
title	Prediction of polyethylene density from FTIR and Raman spectroscopy using multivariate data analysis
title_short	Prediction of polyethylene density from FTIR and Raman spectroscopy using multivariate data analysis
title_full	Prediction of polyethylene density from FTIR and Raman spectroscopy using multivariate data analysis
title_fullStr	Prediction of polyethylene density from FTIR and Raman spectroscopy using multivariate data analysis
title_full_unstemmed	Prediction of polyethylene density from FTIR and Raman spectroscopy using multivariate data analysis
title_sort	prediction of polyethylene density from ftir and raman spectroscopy using multivariate data analysis
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/54e7b65ebc91413eb9d8d5610b817ee0
work_keys_str_mv	AT mbredacs predictionofpolyethylenedensityfromftirandramanspectroscopyusingmultivariatedataanalysis AT cbarretta predictionofpolyethylenedensityfromftirandramanspectroscopyusingmultivariatedataanalysis AT lfcastillon predictionofpolyethylenedensityfromftirandramanspectroscopyusingmultivariatedataanalysis AT afrank predictionofpolyethylenedensityfromftirandramanspectroscopyusingmultivariatedataanalysis AT goreski predictionofpolyethylenedensityfromftirandramanspectroscopyusingmultivariatedataanalysis AT gpinter predictionofpolyethylenedensityfromftirandramanspectroscopyusingmultivariatedataanalysis AT sgergely predictionofpolyethylenedensityfromftirandramanspectroscopyusingmultivariatedataanalysis
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Prediction of polyethylene density from FTIR and Raman spectroscopy using multivariate data analysis

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