6 Materialchemie
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The increasing enrichment of water bodies and soils with plastic waste leads to the accumulation of microscopic plastic particles, so-called microplastics (MP). There is an urgent need for analytical methods that help to identify and quantify MP. At present, mainly thermo-analytical and microscopic methods such as micro-infrared spectroscopy or micro-Raman are used for this purpose. The latter are usually tied to time-consuming sample enrichment and preparation, only small sample quantities (micrograms) can be examined and the evaluation of the obtained spectra can be demanding. In the context of this presentation, two approaches are presented which, via the multi-variate analysis of spectroscopic data, allow i) a new methodological approach to screening MP in contaminated soils and ii) an alternative evaluation of large (micro)-spectroscopic data sets.
First, a NIR spectroscopic method is presented which allows MP consisting of polyethylene, polyethylene terephthalate, polypropylene and polystyrene to be detected in the range of up to 0.5 mass percent. Due to short measurement times and robust technology, this approach has the potential, in contrast to thermo-analytical and micro-spectroscopic methods, to examine larger sample quantities with minimal pre-treatment.
The second approach deals with the evaluation of large data sets, as typically obtained as a result of micro-FTIR using modern FPA detectors. The micro-FTIR technique is based on the spectral recording, imaging and subsequent identification of vibration bands typical of synthetic polymers. The image data sets are large and contain spectra of numerous particles of natural and synthetic origin. Exploratory multivariate data analysis has been tested to complement existing approaches based on e.g. spectrum library searches. The core concept used was dimensionality reduction. The results not only represent an orthogonal method for checking the results obtained by an automated library search, but also revealed a group of spectra that were not recorded in the existing spectrum libraries.
µ-FTIR spectroscopy is a widely used technique in microplastics research. It allows to simultaneously characterize the material of the small particles, fibers or fragments, and to specify their size distribution and shape. Modern detectors offer the possibility to perform two-dimensional imaging of the sample providing detailed information. However, datasets are often too large for manual evaluation calling for automated microplastic identification. Library search based on the comparison with known reference spectra has been proposed to solve this problem.
To supplement this ‘targeted analysis’, an exploratory approach was tested. Principal component analysis (PCA) was used to drastically reduce the size of the data set while maintaining the significant information. Groups of similar spectra in the prepared data set were identified with cluster analysis. Members of different clusters could be assigned to different polymer types whereas the variation observed within a cluster gives a hint on the chemical variability of microplastics of the same type. Spectra labeled according to the respective cluster can be used for supervised learning. The obtained classification was tested on an independent data set and results were compared to the spectral library search approach.