Recycling-oriented characterization of the PET waste stream by SWIR hyperspectral imaging and variable selection methods

The proposed study was carried out to develop a fast and efficient strategy for plastic waste sensor-based sorting in recycling plants, based on hyperspectral imaging (HSI), combined with variable selection methods, to produce a high-quality recycled polyethylene terephthalate (PET) flakes stream. Variable selection techniques were applied in order to identify a limited number of spectral bands useful to recognize the presence of other plastic materials, considered as contaminant, inside a stream of recycled PET flakes, reducing processing time as requested by sorting online applications. Post-consumer plastic samples were acquired by HSI working in the short-wave infrared (SWIR) range (1000-2500 nm). As a first step, the hypercubes were processed applying chemometric logics to build a partial least squares dis-criminant analysis (PLS-DA) classification model using the full investigated spectral range, able to identify PET and contaminant classes. As a second step, two different variable selection methods were then applied, i.e., interval PLS-DA (i-PLSDA) and variable importance in projection (VIP) scores, in order to identify a limited number of spectral bands useful to recognize the two classes and to evaluate the best meth-od, showing efficiency values close to those obtained by the full spectrum model. The best result was achieved by the VIP score method with an average efficiency value of 0.98. The obtained results suggested that the variables selection method can represent a powerful approach for the sensor-based sorting online, decreasing the amount of data to be processed and thus enabling faster recognition compared to the full spectrum model.