A semi-automated Raman micro-spectroscopy method for morphological and chemical characterizations of microplastic litter. Frère, L., Paul-Pont, I., Moreau, J., Soudant, P., Lambert, C., Huvet, A., & Rinnert, E. Marine Pollution Bulletin, 113(1–2):461--468, 2016. 00000
A semi-automated Raman micro-spectroscopy method for morphological and chemical characterizations of microplastic litter [link]Paper  doi  abstract   bibtex   
Every step of microplastic analysis (collection, extraction and characterization) is time-consuming, representing an obstacle to the implementation of large scale monitoring. This study proposes a semi-automated Raman micro-spectroscopy method coupled to static image analysis that allows the screening of a large quantity of microplastic in a time-effective way with minimal machine operator intervention. The method was validated using 103 particles collected at the sea surface spiked with 7 standard plastics: morphological and chemical characterization of particles was performed in < 3 h. The method was then applied to a larger environmental sample (n = 962 particles). The identification rate was 75% and significantly decreased as a function of particle size. Microplastics represented 71% of the identified particles and significant size differences were observed: polystyrene was mainly found in the 2–5 mm range (59%), polyethylene in the 1–2 mm range (40%) and polypropylene in the 0.335–1 mm range (42%).
@article{frere_semi-automated_2016,
	title = {A semi-automated {Raman} micro-spectroscopy method for morphological and chemical characterizations of microplastic litter},
	volume = {113},
	issn = {0025-326X},
	url = {http://www.sciencedirect.com/science/article/pii/S0025326X16308682},
	doi = {10.1016/j.marpolbul.2016.10.051},
	abstract = {Every step of microplastic analysis (collection, extraction and characterization) is time-consuming, representing an obstacle to the implementation of large scale monitoring. This study proposes a semi-automated Raman micro-spectroscopy method coupled to static image analysis that allows the screening of a large quantity of microplastic in a time-effective way with minimal machine operator intervention. The method was validated using 103 particles collected at the sea surface spiked with 7 standard plastics: morphological and chemical characterization of particles was performed in \&lt; 3 h. The method was then applied to a larger environmental sample (n = 962 particles). The identification rate was 75\% and significantly decreased as a function of particle size. Microplastics represented 71\% of the identified particles and significant size differences were observed: polystyrene was mainly found in the 2–5 mm range (59\%), polyethylene in the 1–2 mm range (40\%) and polypropylene in the 0.335–1 mm range (42\%).},
	number = {1–2},
	urldate = {2017-01-02TZ},
	journal = {Marine Pollution Bulletin},
	author = {Frère, L. and Paul-Pont, I. and Moreau, J. and Soudant, P. and Lambert, C. and Huvet, A. and Rinnert, E.},
	year = {2016},
	note = {00000},
	keywords = {ACL, Automating, Environmental monitoring, Microplastics, Morphology, Raman micro-spectroscopy, Surface seawater, panorama},
	pages = {461--468}
}
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