Effective Recycling Solutions for the Production of High-Quality PET Flakes Based on Hyperspectral Imaging and Variable Selection

Abstract

In this study, effective solutions for polyethylene terephthalate (PET) recycling based on hyperspectral imaging (HSI) coupled with variable selection method, were developed and optimized. Hyperspectral images of post-consumer plastic flakes, composed by PET and small quantities of other polymers, considered as contaminants, were acquired in the short-wave infrared range (SWIR: 1000-2500 nm). Different combinations of preprocessing sets coupled with a variable selection method, called competitive adaptive reweighted sampling (CARS), were applied to reduce the number of spectral bands useful to detect the contaminants in the PET flow stream. Prediction models based on partial least squares-discriminant analysis (PLS-DA) for each preprocessing set, combined with CARS, were built and compared to evaluate their efficiency results. The best performance result was obtained by a PLS-DA model using multiplicative scatter correction + derivative + mean center preprocessing set and selecting only 14 wavelengths out of 240. Sensitivity and specificity values in calibration, cross-validation and prediction phases ranged from 0.986 to 0.998. HSI combined with CARS method can represent a valid tool for identification of plastic contaminants in a PET flakes stream increasing the processing speed as requested by sensor-based sorting devices working at industrial level.

Keywords: PET; SWIR; circular economy; hyperspectral imaging; plastic recycling; sensor-based sorting; variable selection.

Figure 1

Source images of calibration dataset showing PET (red square) and contaminant (green square) flakes (a) and source image of the prediction dataset showing PET and contaminant flakes (the latter marked by green circles) (b).

Figure 2

Sisuchema XL^TM chemical imaging workstation (Specim Ltd.).

Figure 3

Average raw spectra and standard deviation of PET (a) and contaminant (b) classes in the SWIR range.

Figure 3

Average raw spectra and standard deviation of PET (a) and contaminant (b) classes in the SWIR range.

Figure 4

Average pre-processed spectra of PET and contaminant classes in the SWIR range adopting three sets of preprocessing techniques: Set 1: Detrend + Smoothing + MC (a); Set 2: SNV + MC (b); and Set 3: MSC + Derivative + MC (c).

Figure 4

Average pre-processed spectra of PET and contaminant classes in the SWIR range adopting three sets of preprocessing techniques: Set 1: Detrend + Smoothing + MC (a); Set 2: SNV + MC (b); and Set 3: MSC + Derivative + MC (c).

Figure 5

PCA results for the preprocessing Set 1 (Detrend + Smoothing + MC): PCA score plot (PC1-PC2) (a) and loadings plot of PC1 and PC2 related to PET and contaminant classes (b).

Figure 6

PCA results for the preprocessing Set 2 (SNV + MC): PCA score plot (PC1-PC2) (a) and loadings plot of PC1 and PC2 related to PET and contaminant classes (b).

Figure 7

PCA results for preprocessing Set 3 (MSC + Derivative + MC): PCA score plot (PC1-PC2) (a) and loadings plot of PC1 and PC2 related to PET and contaminant classes (b).

Figure 8

Prediction maps as resulting from the preliminary utilization of the 3 different preprocessing strategies (i.e., Set 1: Detrend + Smoothing + MC, Set 2: SNV + MC and Set 3: MSC + Derivative + MC) applied to the reduced set of wavelengths, resulting from CARS processing, and the further PLS-DA modeling. Prediction maps related to the utilized wavelengths as resulting from preprocessing Set 1: Detrend + Smoothing + MC (a), Set 2: SNV + MC (b) and Set 3: MSC + Derivative + MC (c).

Figure 9

Prediction maps for the full spectrum PLS-DA model of preprocessing Set 3 (MSC + Derivative + MC).