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Identification of common textile microplastics via autofluorescence spectroscopy coupled with k-means cluster analysis
Summary
This research demonstrated that autofluorescence spectroscopy combined with k-means cluster analysis can successfully identify common textile polymer types in greywater from laundry, using a minimal input dataset. The method accounts for the effects of photooxidation and dyes on spectral signatures, offering a practical approach for microplastic fiber identification.
This research proposes autofluorescence spectroscopy for the successful identification of common polymers present in greywater from a minimal input data set. The effects of photooxidation and dyes are also considered.