Fast and effective classification of plastic waste by pushbroom hyperspectral sensor coupled with hierarchical modelling and variable selection

•Identification of a robust and efficient strategy based on HSI for classification of plastic waste.•Plastic waste hierarchical classification by short-wave infrared hyperspectral imaging (SWIR).•Discrimination of plastic flakes in automated and non-destructive way.•Chemometric strategies applied to...

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Bibliographic Details
Published in:Resources, conservation and recycling conservation and recycling, 2023-10, Vol.197, p.107068, Article 107068
Main Authors: Bonifazi, Giuseppe, Capobianco, Giuseppe, Serranti, Silvia
Format: Article
Language:English
Subjects:
Circular economy
Hyperspectral imaging
Plastic waste
Polymer recycling
Quality control
Sensor-based sorting
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Summary:•Identification of a robust and efficient strategy based on HSI for classification of plastic waste.•Plastic waste hierarchical classification by short-wave infrared hyperspectral imaging (SWIR).•Discrimination of plastic flakes in automated and non-destructive way.•Chemometric strategies applied to hyperspectral images for plastic waste identification.•Wavelengths selection and reduction for plastic flakes classification. Plastic waste management represents a global challenge in the framework of sustainable production and consumption of resources. One of the most critical issues in plastic recycling is polymer separation, necessary to obtain high-quality secondary raw material flow streams. The aim of this work was to build a classification strategy, based on pushbroom hyperspectral imaging, able to recognize the most common polymers found in mixed plastic waste to be applied at recycling plant scale. After exploring polymer spectral differences by principal component analysis, a hierarchical partial least squares-discriminant analysis, based on the acquired full spectra, and a hierarchical interval partial least squares-discriminant analysis, based on selected variables, were tested and their performances were evaluated and compared. High quality classification results were obtained in both cases, demonstrating that the developed multi-class models can be utilized in a flexible way for quality control and/or for on-line sorting actions in recycling plants. [Display omitted]
ISSN:0921-3449
1879-0658
DOI:10.1016/j.resconrec.2023.107068