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A contribution to the harmonization of microplastic analysis in beverages and food
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Researchers developed a transparent quality assurance and quality control (QA/QC) framework for hyperspectral microplastic data analysis in beverages and food, using automated evaluation of a ground truth reference image to validate analytical results. The work addresses a significant gap in harmonization efforts where QA/QC for the data analysis step has been largely neglected despite its importance to inter-laboratory comparability.
Abstract Sampling, sample preparation and particle detection are the key steps in microplastics (MP) analysis. In order to harmonize MP analysis, implementing strict measures for quality assurance and control (QA/QC) for all steps is key. However, especially QA and QC for the analysis of hyperspectral MP data has remained widely neglected. To fill this gap, a transparent and detailed QA/QC method for data analysis based on the automated evaluation of a ground truth reference image is presented.
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