Applying confocal Raman spectroscopy and different linear multivariate analyses to sort polyethylene residues

Abstract

High-Density Polyethylene (HDPE), Low-Density Polyethylene (LDPE), Polypropylene (PP), and recycled Poly(Ethylene Terephthalate) (PET) are widely applied as packaging materials worldwide, and they correspond to more than 50 % of the plastic residues present in municipal solid waste. Currently, it is needed to develop effective methods to ensure the identification and sorting of plastic waste to guarantee necessary purity to obtain quality and economically viable recycled goods from post-consumer plastic. As an attempt to that, we investigated the applicability of Confocal Raman Scattering Spectroscopy (Confocal Raman) combined with Principal Component Analysis (PCA), Linear Partial Least Squares Regression by Intervals (iPLS-R) and Competitive Adaptive Weighted Sampling (CARS/PLS-R) as fast chemometric tools to identify and classify pristine and recycled mixtures of HDPE and LDPE from municipal solid waste in São Paulo, Brazil. We found several limitations in applying this procedure to classify the different polyethylenes and their polymer blends using the Principal Components (PC) from PCA analysis. iPLS-R regression model presents more effectiveness than the CARS/PLS-R model to detect the LDPE content in recycled HDPE/LDPE blends, both being influenced by different contaminants (PP, PET, SiO2, and CaCO3) added in these recycled plastics.