Predicting LDPE/HDPE blend composition by CARS-PLS regression and confocal Raman spectroscopy
Silva, Daniel José da; Wiebeck, Hélio
Abstract
Industries and the scientific community currently focus on creating new ways to recycle and to reuse polymer waste that leads to serious socio-environmental risks. However, the quality of recycled polyethylenes depends strongly on their purity degree, but the distinction between Low Density Polyethylene (LDPE) and High Density Polyethylene (HDPE) by a fast and consistently good methodology is still an unsolved issue for the current recycling processes. In this study, confocal Raman spectroscopy and Competitive Adaptive Reweighted Sampling - Partial Least Squares (CARS-PLS) linear regression have been successfully applied to quantify the concentration of LDPE/HDPE blends. The effects of several regression parameters (pretreatment method, Monte Carlo sampling runs, k-fold and maximal number of latent variables for cross-validation) on the CARS-PLS model training and prediction performance were analyzed. The CARS-PLS-based models show root-mean-squared prediction error of 4.06 - 8.87 wt% of LDPE for the whole composition range of HDPE/LDPE blend.
Keywords
References
1 Gulmine, J. V., Janissek, P. R., Heise, H. M., & Akcelrud, L. (2002). Polyethylene characterization by FTIR. Polymer Testing, 21(5), 557-563. http://dx.doi.org/10.1016/S0142-9418(01)00124-6.
2 Achilias, D. (Ed.). (2012). Material recycling: trends and perspectives . Rijeka: InTech. http://dx.doi.org/10.5772/2003.
3 Al-Salem, S. M., Lettieri, P., & Baeyens, J. (2009). Recycling and recovery routes of plastic solid waste (PSW): a review. Waste Management, 29(10), 2625-2643. http://dx.doi.org/10.1016/j.wasman.2009.06.004. PMid:19577459.
4 Fu, Q., Men, Y., & Strobl, G. (2003). Understanding of the tensile deformation in HDPE/LDPE blends based on their crystal structure and phase morphology. Polymer , 44(6), 1927-1933. http://dx.doi.org/10.1016/S0032-3861(02)00940-0.
5 Al-Salem, S. M., Lettieri, P., & Baeyens, J. (2010). The valorization of Plastic Solid Waste (PSW) by primary to quaternary routes: from re-use to energy and chemicals. Progress in Energy and Combustion Science, 36(1), 103-129. http://dx.doi.org/10.1016/j.pecs.2009.09.001.
6 Associação Brasileira da Indústria de Plástico – ABIPLAST. (2016). Perfil 2016 da indústria brasileira de transformação de material plástico. São Paulo: ABIPLAST.
7 Coutinho, F. M. B., Mello, I. L., & Santa Maria, L. C. (2003). Polietileno: principais tipos, propriedades e aplicações. Polímeros: Ciência e Tecnologia, 13(1), 1-13. http://dx.doi.org/10.1590/S0104-14282003000100005.
8 Pereira, R. A., Mano, E. B., Dias, M. L., & Acordi, E. B. (1997). Comparative study on the lamellar crystal structure of high and low density polyethylenes. Polymer Bulletin , 38(6), 707-714. http://dx.doi.org/10.1007/s002890050109.
9 Billmeyer, F. W. (1984). Textbook of polymer science. New York: John Wiley & Sons.
10 Bhunia, K., Sablani, S. S., Tang, J., & Rasco, B. (2013). Migration of chemical compounds from packaging polymers during microwave, conventional heat treatment, and storage. Comprehensive Reviews in Food Science and Food Safety, 12(5), 523-545. http://dx.doi.org/10.1111/1541-4337.12028.
11 Munaro, M., & Akcelrud, L. (2008). Correlations between composition and crystallinity of LDPE/HDPE blends. Journal of Polymer Research, 15(1), 83-88. http://dx.doi.org/10.1007/s10965-007-9146-2.
12 Perna, G., Lasalvia, M., & Capozzi, V. (2016). Vibrational spectroscopy of synthetic and natural eumelanin. Polymer International, 65(11), 1323-1330. http://dx.doi.org/10.1002/pi.5182.
13 Wold, S., Sjöström, M., & Eriksson, L. (2001). PLS-regression: a basic tool of chemometrics. Chemometrics and Intelligent Laboratory Systems , 58(2), 109-130. http://dx.doi.org/10.1016/S0169-7439(01)00155-1.
14 Rocha, J. T. C., Oliveira, L. M. S. L., Dias, J. C. M., Pinto, U. B., Marques, M. D. L. S. P., Oliveira, B. P., Filgueiras, P. R., Castro, E. V. R., & Oliveira, M. A. L. (2016). Sulfur determination in brazilian petroleum fractions by mid-infrared and near-infrared spectroscopy and partial least squares associated with variable selection methods. Energy & Fuels , 30(1), 698-705. http://dx.doi.org/10.1021/acs.energyfuels.5b02463.
15 Mehmood, T., Liland, K. H., Snipen, L., & Sæbø, S. (2012). A review of variable selection methods in Partial Least Squares Regression. Chemometrics and Intelligent Laboratory Systems, 118, 62-69. http://dx.doi.org/10.1016/j.chemolab.2012.07.010.
16 Li, H., Liang, Y., Xu, Q., & Cao, D. (2009). Key wavelengths screening using competitive adaptive reweighted sampling method for multivariate calibration. Analytica Chimica Acta, 648(1), 77-84. http://dx.doi.org/10.1016/j.aca.2009.06.046. PMid:19616692.
17 Savitzky, A., & Golay, M. J. E. (1964). Smoothing and differentiation of data by simplified least squares procedures. Analytical Chemistry, 36(8), 1627-1639. http://dx.doi.org/10.1021/ac60214a047.
18 Li, H., Xu, Q., & Liang, Y. (2014). libPLS: an integrated library for partial least squares regression and discriminant analysis. PeerJ Preprints, 2, e190v1. http://doi.org/10.7287/peerj.preprints.190v1.
19 Ferrão, M. F., Viera, M. D. S., Pazos, R. E. P., Fachini, D., Gerbase, A. E., & Marder, L. (2011). Simultaneous determination of quality parameters of biodiesel/diesel blends using HATR-FTIR spectra and PLS, iPLS or siPLS regressions. Fuel, 90(2), 701-706. http://dx.doi.org/10.1016/j.fuel.2010.09.016.
20 Silva, D. J., & Wiebeck, H. (2017). Using PLS, iPLS and siPLS linear regressions to determine the composition of LDPE/HDPE blends: a comparison between confocal Raman and ATR-FTIR spectroscopies. Vibrational Spectroscopy, 92, 259-266. http://dx.doi.org/10.1016/j.vibspec.2017.08.009.
21 Allen, V., Kalivas, J. H., & Rodriguez, R. G. (1999). Post-consumer plastic identification using raman spectroscopy. Applied Spectroscopy, 53(6), 672-681. http://dx.doi.org/10.1366/0003702991947324.
22 Bentley, P., & Hendra, P. (1995). Polarised FT Raman studies of an ultra-high modulus polyethylene rod. Spectrochimica Acta. Part A: Molecular and Biomolecular Spectroscopy , 51(12), 2125-2131. http://dx.doi.org/10.1016/0584-8539(95)01513-3.
23 Snyder, R. G., & Kim, Y. (1991). Conformation and low-frequency isotropic Raman spectra of the liquid n-alkanes C4-C9. Journal of Physical Chemistry, 95(2), 602-610. http://dx.doi.org/10.1021/j100155a022.
24 Gall, M. J., Hendra, P. J., Peacock, O. J., Cudby, M. E. A., & Willis, H. A. (1972). The laser-Raman spectrum of polyethylene: the assignment of the spectrum to fundamental modes of vibration. Spectrochimica Acta. Part A: Molecular and Biomolecular Spectroscopy , 28(8), 1485-1496. http://dx.doi.org/10.1016/0584-8539(72)80118-1.
25 Zhang, D., Shen, Y., & Somorjai, G. (1997). Studies of surface structures and compositions of polyethylene and polypropylene by IR+visible sum frequency vibrational spectroscopy. Chemical Physics Letters, 281(4-6), 394-400. http://dx.doi.org/10.1016/S0009-2614(97)01311-0.
26 van den Berg, R. A., Hoefsloot, H. C., Westerhuis, J. A., Smilde, A. K., & van der Werf, M. J. (2006). Centering, scaling, and transformations: improving the biological information content of metabolomics data. BMC Genomics, 7(1), 142. http://dx.doi.org/10.1186/1471-2164-7-142. PMid:16762068.
27 da Silva, D. J., & Wiebeck, H. (2018). CARS-PLS regression and ATR-FTIR spectroscopy for eco-friendly and fast composition analyses of LDPE/HDPE blends. Journal of Polymer Research, 25(5), 112. http://dx.doi.org/10.1007/s10965-018-1507-5.
28 Rosipal, R. (2011). Nonlinear partial least squares an overview. In H. Lodhi & Y. Yamanishi (Ed.), Chemoinformatics and advanced machine learning perspectives (pp. 169-189). Hershey: IGI Global. . http://dx.doi.org/10.4018/978-1-61520-911-8.ch009.
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