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One tool to test it all: The harmonized analysis of microplastics by siMPle
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This meta-analysis introduces a harmonized software tool called siMPle for analyzing microplastics using spectroscopy, addressing the major problem of inconsistent methods across studies. Standardized analysis tools are essential because without them, scientists cannot reliably compare microplastic contamination levels across different locations, making it harder to assess the true scope of human exposure.
While in the past decade the number of studies on microplastics (MPs) rapidly increased the meta-analysis of the generated data is hampered by a missing harmonization of the reported results. This hampers the broad scale assessment of the overall risk of these particles. To achieve comparable results to foster policy advice and decision making, the generation of harmonized data is of high importance. In the case of MP analysis, different spectroscopical methods like Fourier-Transform Infrared (FTIR) or quantum cascade laser (QCL) based infra-red (IR) imaging are majorly used. While it allows a bias-free chemical mapping of the sample, a comparable and harmonized spectral data determination is challenging due to the different manufacturers and commercial software tools available. This generates a broad diversity in the reported data for derived material types and particle sizes. To overcome these limitations, we developed the freeware software tool siMPle (www.simple-plastics.eu). It has been applied in a wide variety of studies covering samples from air, biota, waters, sediments and soils. Here, it handled large datasets from various manufacturers by two automated analysis pipelines. We report on different application scenarios of the software ranging from data analysis harmonization in large scientific projects like JPI-Oceans FACTS towards its application in citizen science projects to harmonize the spectral identification of microplastics. This will be combined with a short outlook on lessons learned using a harmonized analytical tool over a broad series of projects and study areas. Finally, due to the increasing number of data analysis tools based on machine learning or artificial intelligence, we will provide a preview on a link between siMPle and the ability to run custom Python code within the program. This will allow the analysis of large datasets containing several million spectra with relative ease using custom-made analysis tools while using the siMPle graphical interface and data reporting tools. At the same time, comparing results to previous studies or projects like is possible allowing the harmonization of MP data analysis for spectroscopic data for future research.
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A new free software tool called siMPle was developed to standardize microplastic identification from FTIR spectroscopy across instruments from different manufacturers, using a shared database and automated analysis pipeline. Testing across four different instrument types confirmed the tool produces consistent and comparable results, addressing a major bottleneck in microplastics monitoring.
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Researchers proposed and tested methods to rescale microplastic data collected using different sampling and analysis approaches, addressing the lack of standardization that hampers comparisons across studies. They developed correction factors to account for differences in particle size ranges, sampling volumes, and analytical techniques. The study provides practical tools for harmonizing microplastic research data, which could significantly improve global pollution assessments.
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