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Hyperspectral imaging systems (HSI) and chemometric methods for the rapid and direct detection of microplastics

Zenodo (CERN European Organization for Nuclear Research) 2024 Score: 35 ? 0–100 AI score estimating relevance to the microplastics field. Papers below 30 are filtered from public browse.

Emilio Catelli, Moritz Bigalke Moritz Bigalke Moritz Bigalke Moritz Bigalke Moritz Bigalke Moritz Bigalke Giorgia Sciutto, Giorgia Sciutto, Moritz Bigalke Cristina Malegori, Giorgia Sciutto, Giorgia Sciutto, Emilio Catelli, Emilio Catelli, Moritz Bigalke Moritz Bigalke Moritz Bigalke Silvia Prati, Moritz Bigalke Cristina Malegori, Moritz Bigalke Giorgia Sciutto, Cristina Malegori, Moritz Bigalke Cristina Malegori, Silvia Prati, Moritz Bigalke Moritz Bigalke Silvia Prati, Silvia Prati, Moritz Bigalke Moritz Bigalke Moritz Bigalke Moritz Bigalke Moritz Bigalke Moritz Bigalke Moritz Bigalke Moritz Bigalke Moritz Bigalke Moritz Bigalke Moritz Bigalke Moritz Bigalke Silvia Prati, Silvia Prati, Rocco Mazzeo, J. Weber Collin, Rocco Mazzeo, J. Weber Collin, J. Weber Collin, Rocco Mazzeo, J. Weber Collin, Rocco Mazzeo, Giorgia Sciutto, Moritz Bigalke Giorgia Sciutto, Giorgia Sciutto, Moritz Bigalke Moritz Bigalke Moritz Bigalke Moritz Bigalke Rocco Mazzeo, Silvia Prati, Alessandro Tocchio, Alessandro Tocchio, Alessandro Tocchio, Alessandro Tocchio, Silvia Prati, Rocco Mazzeo, Rocco Mazzeo, Michele Occhipinti, Michele Occhipinti, Paolo Oliveri, Paolo Oliveri, Rocco Mazzeo, Paolo Oliveri, Paolo Oliveri, Moritz Bigalke

Summary

Researchers evaluated hyperspectral imaging (HSI) systems combined with chemometric analysis methods as a rapid, direct detection approach for microplastics on filters, avoiding the time-consuming visual pre-sorting and sample purification steps required by conventional spectroscopic methods. The study demonstrated that HSI can identify and map microplastic particles across diverse sample matrices faster and with reduced contamination risk compared to traditional FTIR and Raman approaches.

Polymers

rubber

Models

Invertebrate other

Contamination by microplastics (MP) represents a critical environmental challenge and there is an urgent need to develop methods for the monitoring of particles in different sample matrices. Extensive analytical evaluations are hindered by time and costs associated with to conventional MP spectroscopic analyses. The potentialities of hyperspectral imaging (HSI) systems are being exploited to propose new solutions for the fast and direct detection of MP on filters, avoiding visual pre-sorting or multiple sample purifications, time consuming procedures, airborne contaminations, particle degradation and loss. A near-infrared hyperspectral imaging (NIR-HSI) method is here presented for the detection of MP in surface water and mussel soft tissue samples. NIR-HSI images were acquired recording a short-wave infrared (SWIR) spectrum, for each pixel of the image. An automated normalised difference image (NDI) strategy for data processing was applied, also significantly reducing the time required for data processing and evaluation, and able to enhance spectral differences between the cellulose background of the filter and the polymer of the MP. Additionally, new advances in the characterization of tyre and road wear particles (TRWPs) are presented, by using a new analytical set-up, able to co-register X-ray fluorescence (XRF, 0–48 keV) data, together with visible & near-infrared (VNIR, 400–1100 nm). VNIR spectroscopy enabled the rapid and efficient identification of particles according to their reflectance values. Exploiting these values, masks were created in the visible domain and used to isolate XRF signals specifically from areas where TRWPs were present. Furthermore, the XRF spectra extracted offer a wealth of compositional data that can be submitted to further analysis using multivariate techniques, such as principal component analysis (PCA). These techniques provide researchers with a robust tool for assessing the composition and uniformity of tyre wear particles, enabling them to discern subtle variations and patterns within the data. Also see: https://micro2024.sciencesconf.org/559571/document

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