Black Plastic Waste Classification by Laser-Induced Fluorescence Technique Combined with Machine Learning Approaches

Sensor-based sorting devices commonly used in plastic recycling plants, mainly working in the near infrared range (NIR), are unable to identify black plastics, due to their low spectral reflectance. The aim of this work was to investigate the potentialities offered by laser-induced fluorescence (LIF...

Full description

Saved in:
Bibliographic Details
Published in:Waste and biomass valorization 2024-03, Vol.15 (3), p.1641-1652
Main Authors: Bonifazi, G., Capobianco, G., Cucuzza, P., Serranti, S., Spizzichino, V.
Format: Article
Language:English
Subjects:
Classification
Discriminant analysis
Engineering
Environment
Environmental Engineering/Biotechnology
Fourier transforms
High density polyethylenes
Industrial Pollution Prevention
Infrared spectroscopy
Laser induced fluorescence
Learning algorithms
Machine learning
Original Paper
Plastic debris
Plastics recycling
Polymers
Polypropylene
Polystyrene
Polystyrene resins
Quality control
Recycling
Renewable and Green Energy
Spectral reflectance
Spectrum analysis
Waste management
Waste Management/Waste Technology
Waste streams
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Sensor-based sorting devices commonly used in plastic recycling plants, mainly working in the near infrared range (NIR), are unable to identify black plastics, due to their low spectral reflectance. The aim of this work was to investigate the potentialities offered by laser-induced fluorescence (LIF) technique (spectral range 270–750 nm) for the identification of black polymers inside a plastic waste stream, thus allowing the possibility to build efficient sorting strategies to be applied in recycling plants. Representative samples of black plastics collected among the most utilized in household packaging were selected, constituted by four different types of polymers, i.e., expanded polystyrene (EPS), polystyrene (PS), polypropylene (PP) and high-density polyethylene (HDPE). The acquired LIF spectra were processed using multivariate approaches in order to optimize polymer classification. The developed hierarchical—partial least square-discriminant analysis (Hi-PLS-DA) classification model, showed excellent performances, confirmed by the values of sensitivity and specificity values in prediction, being equal to 1. The correctness of classification obtained by LIF was confirmed by the application of Fourier Transform Infrared spectroscopy (FTIR) on the same samples. The achieved results demonstrated the potential of LIF technique combined with a machine learning approach as sorting/quality control tool of black polymers in recycling plants. Graphical Abstract
ISSN:1877-2641
1877-265X
DOI:10.1007/s12649-023-02146-z