COMPARATIVE STUDY OF CHEMOMETRIC APPROACHES AND MACHINE LEARNING FOR MINIATURIZED NEAR-INFRARED (MICRONIR) SPECTROSCOPY IN PLASTIC WASTE SORTING

• 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:

  tc24-2022.01

Event details: