Work

Cost‐Efficient Detection of Plastics From Post‐Consumer Packaging Waste Using Selected Bands in the Near‐infrared Spectrum

Year: 2025

Type: article

Abstract: ABSTRACT Current polymer identification methods for sorting and recycling purposes, such as near‐infrared (NIR) spectroscopy and hyperspectral imaging, are data‐intensive and costly. Spectral data from hyperspectral images and real waste samples were collected. In the first stage, characteristic wavelength regions for each polymer type were manually selected. Later, the selection process was automated. These regions were then used for model training and characterization of unknown spectra. Integrals of the raw spectra were computed for each wavelength region and further analyzed using the principal component analysis (PCA) method. The trained model is able to successfully allocate unknown spectra using either the k‐nearest neighbors (kNN) algorithm or the convex hull method. The model was further optimized using a systematic parameter study. Results evidence that the model distinguishes between individual plastics and characterizes unknown spectra from real waste with up to 100% accuracy.

Source: Macromolecular Materials and Engineering

DOI: 10.1002/mame.202500143

Authors: Teresa Werner, Michael Dawoud, Doris Aschenbrenner, Iman Taha

Institutions: Hochschule Aalen, Ain Shams University

Language: English

Topic: Spectroscopy and Chemometric Analyses

Subfield: Analytical Chemistry

Field: Chemistry

Domain: Physical Sciences

SDG: Responsible consumption and production

Open Access status: gold

Estimated APC paid: $3,150