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.
Highlights
Accepted: 6 September 2021Plastics represent one of the most used materials, in daily life, in a wide range of applications, due to their peculiar characteristics and low production costs [1]
competitive adaptive reweighted sampling (CARS) was tested as variable selection method after the application of three different preprocessing sequences to identify the best combination for the recognition of contaminants in polyethylene terephthalate (PET)
The results of the variable selection obtained by CARS were evaluated by a partial least squares-discriminant analysis (PLS-DA) model for each set of selected wavelengths
Summary
Plastics represent one of the most used materials, in daily life, in a wide range of applications, due to their peculiar characteristics and low production costs [1]. In order to achieve circular economy and recycling targets, set by European and national legislation, to prevent the environmental impacts of plastic packaging waste, it is essential to implement efficient plastic waste recovery strategies [3,4,5]. Several actions can be taken to improve plastic recycling processes, allowing to bring high-quality recycled products to the market. In this context, the on-line sorting step of the mechanical recycling process plays a preeminent role in order to improve processing performance, increasing recycled plastic quality. A correct recognition and separation of materials in recycling plants is, crucial
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