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Hyperspectral imaging systems (HSI) and chemometric methods for the rapid and direct detection of microplastics
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Researchers developed a near-infrared hyperspectral imaging method using an automated normalized difference image strategy to rapidly detect microplastics in surface water and mussel tissue samples without requiring visual pre-sorting or extensive purification. They also advanced the characterization of tire and road wear particles by co-registering X-ray fluorescence data with visible and near-infrared spectroscopy, enabling rapid identification of particles by chemical composition.
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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