TY - CONF A1 - Wander, Lukas A1 - Vianello, A. A1 - Vollertsen, J. A1 - Braun, Ulrike A1 - Paul, Andrea T1 - Multivariate analysis of large µ-FTIR datasets in search of microplastics N2 - 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. T2 - CEST 2019 CY - Rhodes, Greece DA - 04.09.2019 KW - FTIR KW - Microplastics KW - Multivariate data analysis PY - 2019 AN - OPUS4-48889 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Wander, Lukas A1 - Vianello, A. A1 - Braun, Ulrike A1 - Vollertsen, J. A1 - Paul, Andrea T1 - Analyzing large μ-FTIR data sets in search of microplastics N2 - Working towards a comprehensive understanding of introduction pathways, number, and fate of micro¬plastics in the environment, suitable analytical methods are a precondition. Micro-spectroscopic methods are probably the most widely used techniques. Besides their ability to measure single spectra of a particle or fiber, most modern FTIR- and Raman microscopes are also capable of two-dimensional imaging. This is very appealing to microplastics research because it allows to simultaneously characterize the analytes chemically as well as their size (distribution) and shape. Two-dimensional imaging on extensive sample areas with FTIR-micros¬copes is facilitated by focal plane array (FPA) detectors resulting in large data sets comprised of up to several million spectra. With numbers too large for manual inspection of each individual spectrum, automated data evaluation is inevitable. Identifying different polymers based on the comparison with known reference spectra (library search) has proven to be a suitable approach. For that purpose, FTIR-spectra of common plastics can be collected to create an individual reference library. To Supplement this ‘targeted analysis’, looking for known substances via library search, an exploratory approach was tested. Principal component analysis (PCA) proved to be a helpful tool to drastically reduce the size of the data set while maintaining the significant information. Subsequently, cluster analysis was used to find groups of similar spectra. Spectra found in different clusters could be assigned to different polymer types. The variation observed within clusters gives a hint on chemical variability of microplastics of the same polymer found in the sample. Spectra labeled according to the respective cluster/polymer type were used to build a classification model which allowed to quickly predict the polymer type based on the FTIR spectrum. Classification was tested on a second, independent data set and results were compared to the spectral library search procedure. T2 - ANAKON 2019 CY - Münster, Germany DA - 25.03.2019 KW - Microplastics KW - FTIR KW - Principal component analysis (PCA) PY - 2019 AN - OPUS4-47658 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Wander, Lukas A1 - Vianello, A. A1 - Vollertsen, J. A1 - Westad, F. A1 - Braun, Ulrike A1 - Paul, Andrea T1 - Exploratory analysis of hyperspectral FTIR data obtained from environmental microplastics samples JF - Analytical Methods N2 - Hyperspectral imaging of environmental samples with infrared microscopes is one of the preferred methods to find and characterize microplastics. Particles can be quantified in terms of number, size and size distribution. Their shape can be studied and the substances can be identified. Interpretation of the collected spectra is a typical problem encountered during the analysis. The image datasets are large and contain spectra of countless particles of natural and synthetic origin. To supplement existing Analysis pipelines, exploratory multivariate data analysis was tested on two independent datasets. Dimensionality reduction with principal component analysis (PCA) and uniform manifold approximation and projection (UMAP) was used as a core concept. It allowed for improved visual accessibility of the data and created a chemical two-dimensional image of the sample. Spectra belonging to particles could be separated from blank spectra, reducing the amount of data significantly. Selected spectra were further studied, also applying PCA and UMAP. Groups of similar spectra were identified by cluster analysis using k-means, density based, and interactive manual clustering. Most clusters could be assigned to chemical species based on reference spectra. While the results support findings obtained with a ‘targeted analysis’ based on automated library search, exploratory analysis points the attention towards the group of unidientified spectra that remained and are otherwise easily overlooked. KW - Microplastics KW - FTIR KW - Exploratory analysis PY - 2020 DO - https://doi.org/10.1039/c9ay02483b VL - 12 IS - 6 SP - 781 EP - 791 PB - Royal Society of Chemistry AN - OPUS4-50396 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -