08 Nov 2022 --- Researchers at the Philipp’s University of Marburg, Germany, have made it possible to identify plastics with photoluminescence (PL) spectroscopy and machine learning (ML). The research highlights the necessity of identifying the amount and composition of plastics to understand the impact of plastic litter on the environment. The researchers say they have demonstrated that such a combined approach for plastic identification could rely on photoluminescence spectroscopy, which is analyzed by the study’s ML-based theoretical approach. To do so, they evaluated the capability of ML models to identify plastic and non-plastic materials based on their PL spectra. PL spectroscopy can be used for plastic litter identification. The advantage of this technique is its simplicity, explain the researchers. A set-up consists of a light source that emits monochromatic light in the visible range, a spectrometer and a set of lenses to collect the light emitted from the sample. Since the amount of necessary components is lower compared to other spectroscopy, the acquisition costs for PL spectroscopy are lower.
