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6656665 
Journal Article 
Auto-sorting commonly recovered plastics from waste household appliances and electronics using near-infrared spectroscopy 
Wu, X; Li, Jia; Yao, L; Xu, Z; , 
2020 
1 
Journal of Cleaner Production
ISSN: 0959-6526
EISSN: 1879-1786 
ELSEVIER SCI LTD 
OXFORD 
246 
English 
WOS:000504632600002 
The recycling of plastics from Waste electrical and electronic equipment (WEEE) was constrained by the mix of types. Near-infrared (NIR) spectroscopy is suitable for polymer detection, and it is a rapid, non-destructive analysis method that can be applied to automatic on-line sorting system. The NIR spectra of four commonly recovered WEEE plastics, which are polypropylene (PP), polystyrene (PS), acrylonitrile butadiene styrene (ABS), and acrylonitrile butadiene styrene/polycarbonate (ABS/PC) blend, was collected. The flame-retardant ABS showed difference from ABS in NIR spectra. Three classification methods, which are spectral angle mapper (SAM), partial least squares discriminant analysis (PLS-DA) and linear discriminant analysis combined with principal component analysis (PCA-LDA), was tested. And the classification models trained on the virgin plastics have been compared with the models trained on the WEEE plastic to evaluate how these methods perform under limited training data. PLS-DA is one of the most widely used classification method in spectral data analysis, but it had unsuccessful prediction when the training set only included virgin plastics. But the overall prediction accuracy over 99% could be achieved by the other two whether the training set was the spectra of virgin plastics or WEEE plastics. In general, NIR spectroscopy has the competency of separating WEEE plastics. Finally, an automatic on-line sorting system was designed specifically for the large plastic segments from household appliances and electronics. (C) 2019 Elsevier Ltd. All rights reserved. 
article; NIR spectroscopy; Multivariate analysis; Plastic sorting; Plastic recycling; discriminant analysis; electronic wastes; electronics; flame retardants; household equipment; least squares; near-infrared spectroscopy; nondestructive methods; plastics; polypropylenes; polystyrenes; prediction; principal component analysis; recycling; spectral analysis; styrene