an official journal of: published by:
an official journal of: published by:
Editor in Chief: RAFFAELLO COSSU


  • Giuseppe Bonifazi - Department of Chemical Engineering, Materials & Environment (DICMA), University of Rome La Sapienza, Italy - Research Center For Biophotonics, University of Rome La Sapienza, Italy
  • Giuseppe Capobianco - Department of Chemical Engineering, Materials & Environment (DICMA), University of Rome La Sapienza, Italy
  • Paola Cucuzza - Department of Chemical Engineering, Materials & Environment (DICMA), University of Rome La Sapienza, Italy
  • Silvia Serranti - Department of Chemical Engineering, Materials and Environment, University of Rome La Sapienza, Italy
  • Andrea Uzzo - Research Center For Biophotonics, University of Rome La Sapienza, Italy

Released under CC BY-NC-ND

Copyright: © 2021 CISA Publisher


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 device 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 discriminant analysis (PLSDA) 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 PLSDA (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 method, 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.


Editorial History

  • Received: 02 Dec 2021
  • Revised: 09 Mar 2022
  • Accepted: 11 Mar 2022
  • Available online: 31 Mar 2022


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