| Title |
Fast and effective classification of plastic waste by pushbroom hyperspectral sensor coupled with hierarchical modelling and variable selection |
| ID_Doc |
26943 |
| Authors |
Bonifazi, G; Capobianco, G; Serranti, S |
| Title |
Fast and effective classification of plastic waste by pushbroom hyperspectral sensor coupled with hierarchical modelling and variable selection |
| Year |
2023 |
| Published |
|
| DOI |
10.1016/j.resconrec.2023.107068 |
| Abstract |
Plastic waste management represents a global challenge in the framework of sustainable production and con-sumption 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 ob-tained 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. |
| Author Keywords |
Plastic waste; Polymer recycling; Circular economy; Sensor-based sorting; Quality control; Hyperspectral imaging |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Science Citation Index Expanded (SCI-EXPANDED) |
| EID |
WOS:001055419400001 |
| WoS Category |
Engineering, Environmental; Environmental Sciences |
| Research Area |
Engineering; Environmental Sciences & Ecology |
| PDF |
https://doi.org/10.1016/j.resconrec.2023.107068
|