Instrumental Analytical and Environmental Chemistry, Faculty of Chemistry, Niederrhein University of Applied Sciences , Frankenring 20, D-47798 Krefeld, Germany.
Instrumental Analytical Chemistry and Centre for Water and Environmental Research (ZWU), University of Duisburg-Essen , Universitätsstrasse 5, D-45141 Essen, Germany.
Anal Chem. 2017 Nov 21;89(22):12045-12053. doi: 10.1021/acs.analchem.7b02472. Epub 2017 Nov 7.
One key step studying interactions of microplastics with our ecological system is to identify plastics within environmental samples. Aging processes and surface contamination especially with biofilms impede this characterization. A complex and time-consuming cleaning procedure is a common solution for this problem. However, it implies an artificial change of sample composition with a risk of losing important information or even damaging microplastic particles. In the present work, we introduce a new chemometric approach to identify heavily weathered and contaminated microplastics without any cleaning. The main idea of this concept is based on an automated curve fitting of most relevant vibrational bands to calculate a highly characteristic fingerprint that contains all vibrational band area ratios. This new data set will be used to estimate the similarity of samples and reference standards for identification. A total of 300 individual naturally weathered plastic particles were measured with Fourier transformation infrared spectroscopy in attenuated total reflection mode (FT-IR ATR) and identified successfully with the new method. To that end, all samples were compared with a selection of common reference plastics and bio polymers. As it turns out, the accuracy of identification rises significantly from 76% by means of conventional library searching algorithms to 96% by identifying microplastics with our new method. Therefore, the new approach can be a useful tool to compare and describe similarities of FT-IR spectra of microplastics, which may improve further research studies on this topic.
研究微塑料与生态系统相互作用的一个关键步骤是识别环境样本中的塑料。老化过程和表面污染，特别是生物膜的污染，会阻碍这种特性的识别。一种常见的解决方案是对其进行复杂且耗时的清洁程序。然而，这意味着样品组成会发生人为变化，从而有丢失重要信息甚至损坏微塑料颗粒的风险。在本工作中，我们引入了一种新的化学计量学方法，无需任何清洁即可识别严重风化和污染的微塑料。该概念的主要思想基于对最相关振动带的自动曲线拟合，以计算出高度特征化的指纹，其中包含所有振动带面积比。这个新数据集将用于估计样品与参考标准的相似性，以进行识别。总共测量了 300 个单独的自然风化塑料颗粒，使用衰减全反射模式的傅里叶变换红外光谱（FT-IR ATR）进行测量，并通过新方法成功识别。为此，所有样品均与常见的参考塑料和生物聚合物进行了比较。结果表明，通过传统的库搜索算法进行识别的准确率从 76%显著提高到 96%，通过我们的新方法识别微塑料的准确率。因此，该新方法可以成为比较和描述微塑料 FT-IR 光谱相似性的有用工具，这可能会进一步促进该主题的研究。
