Department of Physics, Philipps-Universität Marburg, Marburg, Germany.
Department of Medicine A, Hematology, Oncology, and Pneumology, University Hospital Münster, Münster, Germany.
Sci Rep. 2022 Nov 6;12(1):18840. doi: 10.1038/s41598-022-23414-3.
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.
需要对全球塑料分布进行定量了解，不仅要评估塑料垃圾对环境的程度和可能影响，还要确定可能的应对措施。对数量和成分进行特征描述的系统数据收集必须基于两个关键组成部分：（i）一种实验方法，该方法足够简单，在全球范围内都可以使用，并且足够明智，可以捕捉到塑料的多样性；（ii）一个能够从大量实验数据中提取相关参数的分析管道。在这项研究中，我们证明通过将光致发光光谱学和基于机器学习的理论分析相结合，可以实现这种方法。我们表明，分类器与降维算法的适当组合能够从光谱数据中识别出特定的材料特性。最佳组合基于一种无监督学习技术，使我们的方法对输入数据的变化具有鲁棒性。
