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Raman imaging to identify microplastics released from toothbrushes: algorithms and particle analysis
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Researchers used Raman imaging to identify and analyze microplastic particles released from toothbrushes during simulated brushing. They found that toothbrushes shed detectable plastic fragments, including nylon and other polymer types, confirming that oral hygiene products can be a direct source of microplastic exposure. The study developed improved algorithms for identifying these particles, which could help standardize microplastic detection in consumer product testing.
Microplastics are small plastic fragments that are of increasing concern due to their potential impacts on the environment and human health. The source of microplastics is not completely clear and might originate in daily lives such as from toothbrushes. When toothbrushes are used to clean teeth, small plastic debris and fragments can be potentially released into mouths directly or environment indirectly. This study aims to examine the release of microplastics from toothbrushes, using Raman imaging to identify and visualise the plastic debris with an increased signal-noise ratio via hyper-spectrum analysis. Using algorithms to convert the hyper-spectrum to an image, the plastic can be distinguished from the co-formulated titanium oxide particles that are not uniformly distributed along the plastics. The non-uniform distribution can lead to the bias results if a single spectrum analysis is conducted at one position rather than imaging analysis to scan an area. The potential false image originating from the off-focal position for the confocal Raman is overcome using the terrain map to guide the Raman imaging. The imaging analysis balancing between the low magnification to capture the overview and the high magnification to test the details is also discussed. While the release amount of microplastics from the toothbrush is estimated at thousands daily with the expected variation, the results of this study have confirmed the release of microplastics in daily lives. The imaging analysis approach along with algorithm can help to identify the chemical elements of microplastics from the complex background, which can benefit the further research on microplastics towards risk assessment and remediation.
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