Prediction of polyethylene density from FTIR and Raman spectroscopy using multivariate data analysis
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In: Polymer Testing, Vol. 104.2021, No. December, 107406, 12.2021.
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TY - JOUR
T1 - Prediction of polyethylene density from FTIR and Raman spectroscopy using multivariate data analysis
AU - Bredács, M.
AU - Barretta, C.
AU - Castillon, L. F.
AU - Frank, A.
AU - Oreski, G.
AU - Pinter, G.
AU - Gergely, S.
N1 - Funding Information: The research work of this paper was performed at the Polymer Competence Center Leoben GmbH (PCCL, Austria) within the framework of the COMET-program of the Federal Ministry for Transport, Innovation and Technology and Federal Ministry for Economy, Family and Youth with contributions by the Department of Polymer Engineering and Science, University of Leoben (Austria) and Department of Applied Biotechnology and Food Science, Budapest University of Technology and Economics (Hungary). The PCCL is funded by the Austrian Government and the State Governments of Styria and Upper Austria.
PY - 2021/12
Y1 - 2021/12
N2 - 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.
AB - 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.
KW - Density prediction
KW - FTIR-ATR and Raman spectroscopy
KW - Multivariate data analysis
KW - PCA and PLS models
KW - Polyethylene
KW - Recycling
UR - http://www.scopus.com/inward/record.url?scp=85119483585&partnerID=8YFLogxK
U2 - 10.1016/j.polymertesting.2021.107406
DO - 10.1016/j.polymertesting.2021.107406
M3 - Article
AN - SCOPUS:85119483585
VL - 104.2021
JO - Polymer Testing
JF - Polymer Testing
SN - 0142-9418
IS - December
M1 - 107406
ER -