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Identifying plastics with photoluminescence spectroscopy and machine learning
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Researchers showed that combining photoluminescence spectroscopy (shining light on plastic and measuring what comes back) with machine learning can reliably identify different types of plastic materials. This low-cost, widely accessible approach could help scientists track and characterize plastic pollution in the environment at a global scale.
A quantitative understanding of the worldwide plastics distribution is required not only to assess the extent and possible impact of plastic litter on the environment but also to identify possible counter measures. A systematic collection of data characterizing amount and composition of plastics has to be based on two crucial components: (i) An experimental approach that is simple enough to be accessible worldwide and sensible enough to capture the diversity of plastics; (ii) An analysis pipeline that is able to extract the relevant parameters from the vast amount of experimental data. In this study, we demonstrate that such an approach could be realized by a combination of photoluminescence spectroscopy and a machine learning-based theoretical analysis. We show that appropriate combinations of classifiers with dimensional reduction algorithms are able to identify specific material properties from the spectroscopic data. The best combination is based on an unsupervised learning technique making our approach robust to alternations of the input data.
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When a large batch of papers lands in the Atlas, we read through it and send a short write-up of what stood out.