Open AccessDETECTING CONTAMINANTS IN POST-CONSUMER PLASTIC PACKAGING WASTE BY A NIR HYPERSPECTRAL IMAGING-BASED CASCADE DETECTION APPROACH
Author(s) -
Giuseppe Bonifazi,
Riccardo Gasbarrone,
Silvia Serranti
Publication year - 2021
Publication title -
detritus
Language(s) - English
Resource type - Journals
eISSN - 2611-4135
pISSN - 2611-4127
DOI - 10.31025/2611-4135/2021.14086
Subject(s) - hyperspectral imaging , sorting , plastic waste , cardboard , waste management , plastic packaging , identification (biology) , environmental science , process engineering , chemometrics , municipal solid waste , raw material , food packaging , computer science , biochemical engineering , materials science , artificial intelligence , engineering , machine learning , chemistry , mechanical engineering , botany , organic chemistry , composite material , biology , programming language
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.