Article
?
AI-assigned paper type based on the abstract. Classification may not be perfect — flag errors using the feedback button.
Tier 2
?
Original research — experimental, observational, or case-control study. Direct primary evidence.
Detection Methods
Sign in to save
A semi-automated Raman micro-spectroscopy method for morphological and chemical characterizations of microplastic litter
Marine Pollution Bulletin2016
176 citations
?
Citation count from OpenAlex, updated daily. May differ slightly from the publisher's own count.
Score: 40
?
0–100 AI score estimating relevance to the microplastics field. Papers below 30 are filtered from public browse.
Laura Frère,
Laura Frère,
Ika Paul-Pont,
Laura Frère,
Laura Frère,
Laura Frère,
Laura Frère,
Laura Frère,
Arnaud Huvet
Arnaud Huvet
Ika Paul-Pont,
Ika Paul-Pont,
Laura Frère,
Ika Paul-Pont,
Ika Paul-Pont,
Laura Frère,
Laura Frère,
Laura Frère,
Ika Paul-Pont,
Ika Paul-Pont,
Ika Paul-Pont,
Ika Paul-Pont,
Ika Paul-Pont,
Ika Paul-Pont,
Ika Paul-Pont,
Ika Paul-Pont,
Ika Paul-Pont,
Arnaud Huvet
Laura Frère,
Ika Paul-Pont,
Philippe Soudant,
Arnaud Huvet
Laura Frère,
Jonathan Moreau,
Christophe Lambert,
Laura Frère,
Christophe Lambert,
Emmanuel Rinnert,
Ika Paul-Pont,
Christophe Lambert,
Ika Paul-Pont,
Ika Paul-Pont,
Philippe Soudant,
Laura Frère,
Jonathan Moreau,
Arnaud Huvet
Laura Frère,
Ika Paul-Pont,
Ika Paul-Pont,
Laura Frère,
Christophe Lambert,
Christophe Lambert,
Christophe Lambert,
Christophe Lambert,
Christophe Lambert,
Christophe Lambert,
Christophe Lambert,
Laura Frère,
Philippe Soudant,
Ika Paul-Pont,
Arnaud Huvet
Laura Frère,
Laura Frère,
Emmanuel Rinnert,
Emmanuel Rinnert,
Arnaud Huvet
Arnaud Huvet
Arnaud Huvet
Christophe Lambert,
Arnaud Huvet
Christophe Lambert,
Arnaud Huvet
Christophe Lambert,
Christophe Lambert,
Ika Paul-Pont,
Arnaud Huvet
Emmanuel Rinnert,
Arnaud Huvet
Arnaud Huvet
Arnaud Huvet
Christophe Lambert,
Christophe Lambert,
Christophe Lambert,
Ika Paul-Pont,
Arnaud Huvet
Philippe Soudant,
Ika Paul-Pont,
Philippe Soudant,
Philippe Soudant,
Arnaud Huvet
Arnaud Huvet
Arnaud Huvet
Arnaud Huvet
Arnaud Huvet
Christophe Lambert,
Arnaud Huvet
Arnaud Huvet
Arnaud Huvet
Arnaud Huvet
Arnaud Huvet
Arnaud Huvet
Arnaud Huvet
Arnaud Huvet
Laura Frère,
Laura Frère,
Emmanuel Rinnert,
Emmanuel Rinnert,
Emmanuel Rinnert,
Christophe Lambert,
Ika Paul-Pont,
Ika Paul-Pont,
Ika Paul-Pont,
Ika Paul-Pont,
Philippe Soudant,
Philippe Soudant,
Philippe Soudant,
Philippe Soudant,
Laura Frère,
Laura Frère,
Arnaud Huvet
Arnaud Huvet
Ika Paul-Pont,
Ika Paul-Pont,
Emmanuel Rinnert,
Ika Paul-Pont,
Laura Frère,
Ika Paul-Pont,
Ika Paul-Pont,
Christophe Lambert,
Ika Paul-Pont,
Arnaud Huvet
Emmanuel Rinnert,
Philippe Soudant,
Philippe Soudant,
Laura Frère,
Ika Paul-Pont,
Philippe Soudant,
Arnaud Huvet
Emmanuel Rinnert,
Philippe Soudant,
Philippe Soudant,
Arnaud Huvet
Philippe Soudant,
Ika Paul-Pont,
Arnaud Huvet
Ika Paul-Pont,
Arnaud Huvet
Ika Paul-Pont,
Christophe Lambert,
Emmanuel Rinnert,
Ika Paul-Pont,
Ika Paul-Pont,
Ika Paul-Pont,
Philippe Soudant,
Arnaud Huvet
Arnaud Huvet
Philippe Soudant,
Philippe Soudant,
Arnaud Huvet
Philippe Soudant,
Arnaud Huvet
Arnaud Huvet
Arnaud Huvet
Arnaud Huvet
Christophe Lambert,
Christophe Lambert,
Christophe Lambert,
Ika Paul-Pont,
Christophe Lambert,
Philippe Soudant,
Christophe Lambert,
Arnaud Huvet
Philippe Soudant,
Ika Paul-Pont,
Arnaud Huvet
Philippe Soudant,
Christophe Lambert,
Ika Paul-Pont,
Philippe Soudant,
Christophe Lambert,
Christophe Lambert,
Arnaud Huvet
Arnaud Huvet
Philippe Soudant,
Arnaud Huvet
Ika Paul-Pont,
Philippe Soudant,
Philippe Soudant,
Arnaud Huvet
Philippe Soudant,
Philippe Soudant,
Ika Paul-Pont,
Ika Paul-Pont,
Emmanuel Rinnert,
Philippe Soudant,
Arnaud Huvet
Arnaud Huvet
Ika Paul-Pont,
Ika Paul-Pont,
Arnaud Huvet
Ika Paul-Pont,
Ika Paul-Pont,
Arnaud Huvet
Arnaud Huvet
Christophe Lambert,
Christophe Lambert,
Arnaud Huvet
Ika Paul-Pont,
Philippe Soudant,
Philippe Soudant,
Philippe Soudant,
Arnaud Huvet
Philippe Soudant,
Ika Paul-Pont,
Arnaud Huvet
Philippe Soudant,
Arnaud Huvet
Arnaud Huvet
Ika Paul-Pont,
Arnaud Huvet
Summary
Researchers developed a semi-automated Raman micro-spectroscopy method coupled with static image analysis for characterizing microplastics, achieving morphological and chemical identification of over 1,000 particles in under three hours, with polyethylene, polystyrene, and polypropylene as the dominant types in the environmental sample.
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 <3h. 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 2-5mm range (59%), polyethylene in the 1-2mm range (40%) and polypropylene in the 0.335-1mm range (42%).