Issue 24, 2018

Identification and quantification of microplastics in table sea salts using micro-NIR imaging methods

Abstract

Microplastics have been one of the most serious environmental pollutants of concern over recent decades. These pollutants have been detected not only in the marine biota, but also in abiotic sea products such as sea salts, which might pose a threat to food safety. Although efficient methods for the analysis of microplastics in different matrices have been published, in many cases, part of the quantification protocol relies on counting by eye, which is inefficient and time consuming. In this study, a new method for the analysis of polyethylene terephthalate (PET) microplastics in table sea salts was developed. After hydrogen peroxide (H2O2) pretreatment and filtration, the PET particles in vacuum-filtered retentates of table sea salts were imaged by micro-FT-NIR imaging and identified using spectral similarity methods including correlation coefficient mapping (CCM), spectral angle mapping (SAM) and Euclidean distance mapping (EDM) methods. The number of particles was acquired by a computer based automatic counting method. The results showed that, in most cases, the number of microplastics obtained by the automatic counting method was equal to that obtained by the counting by eye method, which proved that micro-NIR chemical imaging combined with chemometric methods can be an alternative way to identify and count the microplastics simultaneously. This method for detecting and quantifying PET microplastics in table sea salts could also be applied to other types of polymers in other environmental sample detection.

Graphical abstract: Identification and quantification of microplastics in table sea salts using micro-NIR imaging methods

Supplementary files

Article information

Article type
Paper
Submitted
18 Jan 2018
Accepted
04 Apr 2018
First published
09 Apr 2018

Anal. Methods, 2018,10, 2881-2887

Identification and quantification of microplastics in table sea salts using micro-NIR imaging methods

J. Zhang, K. Tian, C. Lei and S. Min, Anal. Methods, 2018, 10, 2881 DOI: 10.1039/C8AY00125A

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