COMPARATIVE STUDY OF CHEMOMETRIC APPROACHES AND MACHINE LEARNING FOR MINIATURIZED NEAR-INFRARED (MICRONIR) SPECTROSCOPY IN PLASTIC WASTE SORTING |
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C. Marchesi, M. Rani, S. Federici, M. Lancini, L. E. Depero |
- Abstract:
The plastic recycling industry necessitates fast and reliable methods to recognize the different polymer types to improve the recycling capacity. In this contribution, the coupling of a miniaturized Near-Infrared (NIR) spectroscopy technique with a robust data analysis is presented. Comparison of multiple machine learning techniques, such as Support-Vector Machines (SVM), Fine Tree, Bagged Tree, and Ensemble Learning, and chemometric approaches, such as Principal Component Analysis (PCA) and Partial Least Squares – Discriminant Analysis (PLS-DA), were combined to provide a broad overview and a rational means for selecting the approach in analysing NIR data for plastic waste sorting.
- Keywords:
- plastic waste sorting; Near-Infrared Spectroscopy (NIRS); circular economy; machine learning; chemometrics
- Download:
- IMEKO-TC24-2022-01.pdf
- DOI:
- 10.21014/tc24-2022.01
- Event details
- IMEKO TC:
- TC24
- Event name:
- Joint IMEKO TC11 & TC24 hybrid conference
- Title:
Chemical measurements towards a sustainable future
- Place:
- Dubrovnik, CROATIA
- Time:
- 16 October 2022 - 20 October 2022