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Guidance on data library, data treatment algorithms and correlation analysis for automatic classification and recognition tools for SMPs (100-10 μm) in complex food and environmental matrices.
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Researchers developed open-access spectral libraries and correlation-based algorithms for automatic identification of small microplastics (100-10 μm) in food and environmental matrices, using FTIR and Raman spectra combined with physical characteristics to support reliable polymer-type recognition.
In this document we report the realisation of usable libraries containing microplastics characteristics (e.g. FTIR and Raman spectra, decomposition product markers, physical characteristics etc.) to be further used as a support tool for microplastics identification. Moreover, is meant to provide validated strategies of data analysis and utilization for reliable identification and counting microplastics particles in a representative sample, including the comparison of different automatic recognition methods. Recognition algorithms based on correlation coefficients will be applied for specific polymer type identification. First objective is the development of a comprehensive spectral data library for SMPs. Establish an open-access, structured spectral library of synthetic microplastic particles (SMPs), including pristine particles of varying sizes and compositions, and SMPs derived from food and environmental matrices. The library will document spectral variability due to polymer composition (e.g., additives) and environmental weathering, and will be publicly available via the project website and repositories such as Zenodo. Second objective is to design correlation-based algorithms for SMP identification. Design, develop, and validate advanced algorithms to quantify spectral correlations between unknown particles and reference data from the spectral library. The algorithms will include optimized correlation coefficients that emphasize key spectral features and define correlation thresholds to ensure identification sensitivity (true positive rate ≥ 95 %) and quantify false positive rates. Identification uncertainty will be expressed using likelihood ratios. “The project 21GRD07 PlasticTrace has received funding from the European Partnership on Metrology, co-financed from the European Union’s Horizon Europe Research and Innovation Programme and by the Participating States.”• Funder name: European Partnership on Metrology• Funder ID: 10.13039/100019599• Grant number: 21GRD07 PlasticTrace
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