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.
COMPARATIVE STUDY OF CHEMOMETRIC APPROACHES AND MACHINE LEARNING FOR MINIATURIZED NEAR-INFRARED (MICRONIR) SPECTROSCOPY IN PLASTIC WASTE SORTING
Marchesi C.;Rani M.;Federici S.
;Lancini M.
;Depero L. E.
2022-01-01
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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
