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


  • Giuseppe Bonifazi - Department of Chemical Engineering, Materials and Environment, Sapienza University of Rome, Italy
  • Riccardo Gasbarrone - Department of Chemical Engineering, Materials and Environment, Sapienza University of Rome, Italy
  • Silvia Serranti - Department of Chemical Engineering, Materials and Environment, Sapienza University of Rome, Italy


Released under CC BY-NC-ND

Copyright: © 2020 CISA Publisher


Recycling of post-consumer packaging wastes involves a complex chain of activities, usually based on three main stages, that is: i) collection from households or recovery from Municipal solid waste (MSW), ii) sorting and, finally, iii) mechanical recycling. The systematic identification of impurities inside plastic packaging waste streams, and the assessment of the different occurring materials, can be considered as one of the key issues to certify and to classify waste materials fed to recycling plants and to perform a full control of the resulting processed fractions and byproducts, that have to comply with market demands. The utilization of a Near InfraRed (NIR) – HyperSpectral Imaging (HSI) based methods, along with chemometrics and machine learning techniques, can fulfill these goals. In this paper, the HSI-based sorting logics, to apply, to implement and to set up to perform an automatic separation of paper, cardboard, plastics and multilayer packaging are investigated.


Editorial History

  • Received: 15 Dec 2020
  • Revised: 09 Mar 2021
  • Accepted: 12 Mar 2021
  • Available online: 06 Jun 2021


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