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Weathering-independent differentiation of microplastic polymers by reflectance IR spectrometry and pattern recognition
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Researchers developed a weathering-independent method for identifying microplastic polymer types using reflectance infrared spectrometry combined with pattern recognition techniques including principal components analysis and classification trees, demonstrating reliable polymer differentiation even when field samples are weathered or biofouled.
The presence and effects of microplastics in the environment is being continuously studied, so the need for a reliable approach to ascertain the polymer/s constituting them has increased. To characterize them, infrared (IR) spectrometry is commonly applied, either reflectance or attenuated total reflectance (ATR). A common problem when considering field samples is their weathering and biofouling, which modify their spectra. Hence, relying on spectral matching between the unknown spectrum and spectral databases is largely defective. In this paper, the use of IR spectra combined with pattern recognition techniques (principal components analysis, classification and regression trees and support vector classification) is explored first time to identify a collection of typical polymers regardless of their ageing. Results show that it is possible to identify them using a reduced suite of spectral wavenumbers with coherent chemical meaning. The models were validated using two datasets containing artificially weathered polymers and field samples.
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