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Updated: Oct 25, 2025

Protocol for Microplastics Sampling on the Sea Surface and Sample Analysis
Published on: December 16, 2016
Extraction and detection methods of microplastics in food and marine systems: A critical review
Adithya Sridhar1, Deepa Kannan1, Ashish Kapoor1
1Department of Chemical Engineering, Faculty of Engineering and Technology, SRM Institute of Science and Technology, Kattankulathur, Tamil Nadu, 603203, India.
Abstract:
The ubiquitous presence of microplastics as contaminants in the ecosystem has become a matter of environmental concern gaining considerable attention in the research community as well as public arena. Lack of efficient collection and improper management of plastic have resulted in the enormous amounts of plastic wastes landing into the marine systems with oceans being the ultimate sink. Due to non-biodegradability, these plastics break down into smaller fragments over a period of time leading to consumption by aquatic species, threatening marine life. In the recent years, a wide range of food products has also been contaminated with microplastics directly affecting human health. This review focuses on the separation and identification technologies for extraction and detection of microplastics in food and marine ecosystems. Efficient technologies like floatation, membrane separation, chemical treatment, enzymatic treatment, and other miscellaneous techniques have been discussed considering their merits and demerits. Additionally, identification technologies like optical detection, scanning electron microscopy, Fourier transform infrared spectroscopy, Raman spectroscopy, thermo-analytical methods, and hyperspectral imaging have been emphasized for the detection of microplastic particles. The emerging techniques like enzymatic digestion combined with hyperspectral imaging could be a possible way for obtaining higher separation efficiency and characterization with minimal harm to food sample. This article narrows the gap for choosing a standard separation technology for microplastic detection in food matrices keeping in mind the composition, particle size, shape, data visualization techniques and cost.
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