Direct identification and visualisation of real-world contaminating microplastics using Raman spectral mapping with multivariate curve resolution-alternating least squares

Abstract

Microplastics (MPs) contamination is ubiquitous in environmental matrices worldwide. Moreover these pollutants can be ingested by organisms and transported to organs via the circulatory system. Although efficient methods for the analysis of MPs derived from environment matrices and organisms’ tissue samples have been developed after special sample pre-treatment, there remains a need for an optimised approach allowing direct identification and visualisation these MPs in real environmental matrices and organismal samples. Herein, we firstly used a multivariate curve resolution-alternating least squares (MCR-ALS) analysis of Raman hyperspectral imaging data to direct identification and visualisation of MPs in a complex serum background. Four common MPs types including polyethylene (PE), polystyrene (PS), polypropylene (PP) and polyethylene terephthalate (PET) were identified and visualised either individually or in mixtures within spiked samples at an 8-μm spatial resolution. Moreover, Raman imaging based on MCR-ALS was successfully applied in fish faeces biological samples and environmental sand samples for in situ MPs identification directly without washing or removal of organic matter. The current results demonstrate Raman imaging based on MCR-ALS as a novel imaging approach for direct identification and visualisation of MPs, through extraction of MPs’ chemical spectra within a complicated biological or environmental background whilst eliminating overlapping Raman bands and fluorescence interference.