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Identifying plastics with photoluminescence spectroscopy and machine learning
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...
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| Những tác giả chính: | , , , , , |
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| Định dạng: | Bài viết |
| Ngôn ngữ: | Tiếng Anh |
| Được phát hành: |
Philipps-Universität Marburg
2022
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| Những chủ đề: | |
| Truy cập trực tuyến: | Bài toàn văn PDF |
| Các nhãn: |
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| Tóm tắt: | 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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| Mô tả sách: | Gefördert durch den Open-Access-Publikationsfonds der UB Marburg. |
| DOI: | 10.1038/s41598-022-23414-3 |