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Exploratory analysis of hyperspectral FTIR data obtained from environmental microplastics samples
(2020)
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.
A sedimented freshwater suspended organic matter fortified with particles of polyethylene (PE), polypropylene (PP), polystyrene (PS), and polyethylene terephthalate (PET) was employed in an interlaboratory comparison of thermoanalytical methods for microplastics identification and quantification. Three laboratories performed pyrolysis gas chromatography-mass spectrometry (Py-GC-MS), three others provided results using thermal extraction desorption followed by gas chromatography coupled to mass spectrometry (TED-GC-MS). One participant performed thermogravimetry-infrared spectroscopy (TGA-FTIR) and two participants used thermogravimetry coupled to mass spectrometry (TGA-MS). Further participants used differential scanning microscopy (DSC), a procedure based on micro combustion calorimetry (MCC) and a procedure based on elemental analysis.
Each participant employed a different combination of sample treatment, calibration and instrumental Settings for polymer identification and quantification. Though there is obviously room for improvements regarding the between-laboratory reproducibility and the harmonization of procedures it was seen that the participants Performing Py-GC-MS, TED-GC-MS, and TGA-FTIR were able to correctly identify all polymers and to report reasonable quantification results in the investigated concentration range (PE: 20.0 μg/mg, PP: 5.70 μg/mg; PS: 2.20 μg/mg, PET: 18.0 μg/mg). Although for the other methods limitations exists regarding the detection of specific polymers, they showed potential as alternative approaches for polymer quantification in solid environmental matrices.
The increasing pollution of terrestrial and aquatic ecosystems with plastic debris leads to the accumulation of microscopic plastic particles of still unknown amount. To monitor the degree of contamination analytical methods are urgently needed, which help to quantify microplastics (MP). Currently, time-costly purified materials enriched on filters are investigated both by micro-infrared spectroscopy and/or micro-Raman. Although yielding precise results, these techniques are time consuming, and are restricted to the analysis of a small part of the sample in the order of few micrograms. To overcome these problems, here we tested a macroscopic dimensioned NIR process-spectroscopic method in combination with chemometrics. For calibration, artificial MP/soil mixtures containing defined ratios of polyethylene, polyethylene terephthalate, polypropylene, and polystyrene with diameters < 125 µm were prepared and measured by a process FT-NIR spectrometer equipped with a fiber optic reflection probe. The resulting spectra were processed by chemometric models including support vector machine regression (SVR), and partial least squares discriminant analysis (PLS-DA). Validation of models by MP mixtures, MP-free soils and real-world samples, e.g. and fermenter residue, suggest a reliable detection and a possible classification of MP at levels above 0.5 to 1.0 mass% depending on the polymer. The benefit of the combined NIRS chemometric approach lies in the rapid assessment whether soil contains MP, without any chemical pre-treatment. The method can be used with larger sample volumes and even allows for an online prediction and thus meets the demand of a high-throughput method.