Effective Recycling Solutions for the Production of High-Quality PET Flakes Based on Hyperspectral Imaging and Variable Selection
In this study, effective solutions for polyethylene terephthalate (PET) recycling based on hyperspectral imaging (HSI) coupled with variable selection method, were developed and optimized. Hyperspectral images of post-consumer plastic flakes, composed by PET and small quantities of other polymers, c...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-09-01
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| Series: | Journal of Imaging |
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| Online Access: | https://www.mdpi.com/2313-433X/7/9/181 |
| _version_ | 1797518697411117056 |
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| author | Paola Cucuzza Silvia Serranti Giuseppe Bonifazi Giuseppe Capobianco |
| author_facet | Paola Cucuzza Silvia Serranti Giuseppe Bonifazi Giuseppe Capobianco |
| author_sort | Paola Cucuzza |
| collection | DOAJ |
| description | In this study, effective solutions for polyethylene terephthalate (PET) recycling based on hyperspectral imaging (HSI) coupled with variable selection method, were developed and optimized. Hyperspectral images of post-consumer plastic flakes, composed by PET and small quantities of other polymers, considered as contaminants, were acquired in the short-wave infrared range (SWIR: 1000–2500 nm). Different combinations of preprocessing sets coupled with a variable selection method, called competitive adaptive reweighted sampling (CARS), were applied to reduce the number of spectral bands useful to detect the contaminants in the PET flow stream. Prediction models based on partial least squares-discriminant analysis (PLS-DA) for each preprocessing set, combined with CARS, were built and compared to evaluate their efficiency results. The best performance result was obtained by a PLS-DA model using multiplicative scatter correction + derivative + mean center preprocessing set and selecting only 14 wavelengths out of 240. Sensitivity and specificity values in calibration, cross-validation and prediction phases ranged from 0.986 to 0.998. HSI combined with CARS method can represent a valid tool for identification of plastic contaminants in a PET flakes stream increasing the processing speed as requested by sensor-based sorting devices working at industrial level. |
| first_indexed | 2024-03-10T07:33:22Z |
| format | Article |
| id | doaj.art-ed2913d76c5344e98eb6a95c04404006 |
| institution | Directory Open Access Journal |
| issn | 2313-433X |
| language | English |
| last_indexed | 2024-03-10T07:33:22Z |
| publishDate | 2021-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Imaging |
| spelling | doaj.art-ed2913d76c5344e98eb6a95c044040062023-11-22T13:44:12ZengMDPI AGJournal of Imaging2313-433X2021-09-017918110.3390/jimaging7090181Effective Recycling Solutions for the Production of High-Quality PET Flakes Based on Hyperspectral Imaging and Variable SelectionPaola Cucuzza0Silvia Serranti1Giuseppe Bonifazi2Giuseppe Capobianco3Department of Chemical Engineering, Materials & Environment, Sapienza, Rome University, Via Eudossiana 18, 00184 Rome, ItalyDepartment of Chemical Engineering, Materials & Environment, Sapienza, Rome University, Via Eudossiana 18, 00184 Rome, ItalyDepartment of Chemical Engineering, Materials & Environment, Sapienza, Rome University, Via Eudossiana 18, 00184 Rome, ItalyDepartment of Chemical Engineering, Materials & Environment, Sapienza, Rome University, Via Eudossiana 18, 00184 Rome, ItalyIn this study, effective solutions for polyethylene terephthalate (PET) recycling based on hyperspectral imaging (HSI) coupled with variable selection method, were developed and optimized. Hyperspectral images of post-consumer plastic flakes, composed by PET and small quantities of other polymers, considered as contaminants, were acquired in the short-wave infrared range (SWIR: 1000–2500 nm). Different combinations of preprocessing sets coupled with a variable selection method, called competitive adaptive reweighted sampling (CARS), were applied to reduce the number of spectral bands useful to detect the contaminants in the PET flow stream. Prediction models based on partial least squares-discriminant analysis (PLS-DA) for each preprocessing set, combined with CARS, were built and compared to evaluate their efficiency results. The best performance result was obtained by a PLS-DA model using multiplicative scatter correction + derivative + mean center preprocessing set and selecting only 14 wavelengths out of 240. Sensitivity and specificity values in calibration, cross-validation and prediction phases ranged from 0.986 to 0.998. HSI combined with CARS method can represent a valid tool for identification of plastic contaminants in a PET flakes stream increasing the processing speed as requested by sensor-based sorting devices working at industrial level.https://www.mdpi.com/2313-433X/7/9/181PETsensor-based sortingplastic recyclinghyperspectral imagingSWIRvariable selection |
| spellingShingle | Paola Cucuzza Silvia Serranti Giuseppe Bonifazi Giuseppe Capobianco Effective Recycling Solutions for the Production of High-Quality PET Flakes Based on Hyperspectral Imaging and Variable Selection Journal of Imaging PET sensor-based sorting plastic recycling hyperspectral imaging SWIR variable selection |
| title | Effective Recycling Solutions for the Production of High-Quality PET Flakes Based on Hyperspectral Imaging and Variable Selection |
| title_full | Effective Recycling Solutions for the Production of High-Quality PET Flakes Based on Hyperspectral Imaging and Variable Selection |
| title_fullStr | Effective Recycling Solutions for the Production of High-Quality PET Flakes Based on Hyperspectral Imaging and Variable Selection |
| title_full_unstemmed | Effective Recycling Solutions for the Production of High-Quality PET Flakes Based on Hyperspectral Imaging and Variable Selection |
| title_short | Effective Recycling Solutions for the Production of High-Quality PET Flakes Based on Hyperspectral Imaging and Variable Selection |
| title_sort | effective recycling solutions for the production of high quality pet flakes based on hyperspectral imaging and variable selection |
| topic | PET sensor-based sorting plastic recycling hyperspectral imaging SWIR variable selection |
| url | https://www.mdpi.com/2313-433X/7/9/181 |
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