0
Article Tier 2 Detection Methods Marine & Wildlife Policy & Risk Sign in to save

Hyperspectral Imaging and Data Analysis for Detecting and Determining Plastic Contamination in Seawater Filtrates

Journal of Near Infrared Spectroscopy 2016 93 citations

AI summary Read the abstract

Researchers tested whether hyperspectral imaging combined with multivariate data analysis could detect and identify plastic particles on filters from seawater samples, finding the method could locate plastic contamination and distinguish polymer types. This approach could offer a faster and more automated alternative to manual microscopy for environmental microplastic monitoring.

Polymers
Study Type Environmental

One possible way of monitoring plastic particles in sea water is by imaging spectroscopic measurements on filtrates. The idea is that filters from seawater sampling can be imaged in many wavelengths and that a multivariate data analysis can give information on (1) spatial location of plastic material on the filter and (2) composition of the plastic materials. This paper reports on simulated samples, with spiked reference plastic particles and real seawater filtrates containing microplastic pollutants. These real samples were previously identified through visual examination in a microscope. The samples were imaged using three different imaging systems. The different wavelength ranges were 375–970 nm, 960–1662 nm and 1000–2500 nm. Data files from all three imaging systems were analysed by hyperspectral image analysis. The method using the wavelength span 1000–2500 nm was shown to be the most applicable to this specific type of samples and gave a 100% particle recognition on reference plastic, above 300 μm, and an 84% pixel recognition on household polyethylene plastic. When applied to environmental samples the technique showed an increase in identified particles compared with visual investigations. These initial tests indicate a potential underestimation of microplastics in environmental samples. This is the first study to demonstrate that hyperspectral imaging techniques can be used to study microplastics down to 300 μm, which is a common size limit used in microplastic surveys.

More Papers Like This

Article Tier 2

Simple and rapid detection of microplastics in seawater using hyperspectral imaging technology

AI summary Read the abstract

Researchers developed a hyperspectral imaging technique for rapid detection and identification of microplastics in seawater, demonstrating it could analyze multiple particles simultaneously and significantly reduce the time burden compared to traditional individual-particle identification protocols.

Article Tier 2

Hyperspectral imaging based approach for monitoring of microplastics from marine environment

AI summary Read the abstract

Researchers explored using hyperspectral imaging sensors to detect and identify different types of microplastics collected from various sea areas, combining spectral analysis with morphological characterization of particle shape and size. The approach shows potential as an automated monitoring tool for marine microplastic surveillance.

Article Tier 2

The Identification of Spherical Engineered Microplastics and Microalgae by Micro-hyperspectral Imaging

AI summary Read the abstract

Scientists used hyperspectral imaging combined with machine learning to distinguish between microplastic particles and microalgae in seawater samples. Developing reliable automated methods for identifying microplastics in complex environmental samples is critical for accurate contamination monitoring.

Article Tier 2

Detection and identification of microplastics directly in water by hyperspectral imaging

AI summary Read the abstract

Researchers used hyperspectral imaging to identify different types of microplastics mixed together in water, demonstrating that the technique can distinguish polymer types based on their spectral signatures. This non-destructive, real-time method could improve the speed and accuracy of microplastic monitoring in water samples.

Article Tier 2

Hyperspectral Imaging for Detecting Plastic Debris on Shoreline Sands to Support Recycling

AI summary Read the abstract

Researchers explored the use of hyperspectral imaging technology to detect and identify different types of plastic debris on beach sand. The method can distinguish between various polymer types, supporting more efficient recycling and cleanup operations. The study demonstrates a non-contact detection approach that could help prevent further degradation of shoreline plastics into microplastics.

Research digests by email

When a large batch of papers lands in the Atlas, we read through it and send a short write-up of what stood out.

Email me about

Share this paper