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 -