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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 Marine & Wildlife Sign in to save

Miniature Submarine using Near-Infrared Spectroscopy to Detect and Collect Microplastics

International journal of high school research 2022 3 citations ? Citation count from OpenAlex, updated daily. May differ slightly from the publisher's own count.
Brandon Pae, Darson Chen

Summary

This paper proposes a miniature submarine drone equipped with near-infrared (NIR) spectroscopy for underwater detection and collection of microplastics, addressing the challenge that NIR light cannot easily penetrate the ocean surface. The researchers tested NIR spectroscopy's ability to distinguish microplastics from organic material (kombu kelp) using least-squares spectral analysis, finding the method generally effective at determining sample composition. Improvements in spectral curvature analysis and expanded reference datasets are needed before reliable underwater deployment.

Study Type Environmental

A promising solution to detecting and collecting ocean microplastics is utilizing near-infrared (NIR) spectroscopy.NIR spectroscopy is a cost-effective, safe, and accurate method to determine the chemical composition of unknown materials.However, since it cannot easily function underwater as light cannot penetrate the ocean surface, a submarine drone design developed here will employ near-infrared spectroscopy to differentiate the collected micro-objects.In particular, an experiment was conducted where varying sizes of kombu (edible kelp) and microplastics were scanned by a NIR spectrometer to determine the mass composition of a given sample.A least sum of squares method was used to analyze the spectra data from an unknown concentration of microplastics by comparing the spectra data to stored spectra datasets which were produced from samples of known concentrations.The results showed that least squares analysis was a generally effective method to compare such spectra graphs and deduce the mass composition of a given sample.Although, more improvements including the analysis of the spectra graph's curvature and an increased amount of composition data, are necessary to make the approach more accurate.

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