We can't find the internet
Attempting to reconnect
Something went wrong!
Hang in there while we get back on track
A sustainable analytical workflow for microplastic detection and typification via NIR-HSI: Validation through sea salt analysis
AI summary Read the abstract
Researchers developed a sustainable analytical workflow for detecting and classifying microplastics using near-infrared hyperspectral imaging combined with chemometrics. The study validated the method through sea salt analysis, demonstrating a rapid, non-destructive, and solvent-free approach that aligns with green analytical chemistry principles for environmental microplastic monitoring.
This work presents a sustainable analytical workflow for the detection and typification of microplastics (MPs) in environmental matrices using Near Infrared Hyperspectral Imaging (NIR-HSI) combined with chemometrics. The proposed methodology enables rapid, non-destructive, and solvent-free analysis, aligning with green analytical principles. A hierarchical classification strategy based on Partial Least Squares Discriminant Analysis (PLS-DA) was developed to discriminate between salt and MP spectra and subsequently to typify the polymeric nature of the detected MPs. Four of the most prevalent polymers in the Mediterranean Sea (polyethylene (PE), polyethylene terephthalate (PET), polystyrene (PS), and polyvinyl chloride (PVC)) were selected as reference standards. The workflow was first optimised and validated using reference and simulated salts and then applied to real sea salt samples collected from Mediterranean coastal saltworks and commercial grocery salts. The results demonstrated excellent classification performance, with 100 % sensitivity, specificity, and precision in both validation stages. Among the analysed samples, MP contamination was confirmed in 3 coastal and 2 commercial salts, with PET and PE being the dominant polymers. These findings highlight sea salt as a valuable proxy for marine MP contamination and as a potential route of human exposure. Overall, this study introduces a green, efficient, and reproducible analytical approach for MPs detection and typification, providing a foundation for future large-scale environmental monitoring and risk assessment initiatives.
More Papers Like This
A comprehensive and fast microplastics identification based on near-infrared hyperspectral imaging (HSI-NIR) and chemometrics
AI summary Read the abstract
Researchers developed a near-infrared hyperspectral imaging method combined with chemometric analysis for rapid, high-throughput identification of microplastic types in mixed samples, achieving high classification accuracy and offering a faster alternative to FTIR and Raman methods for routine monitoring.
An effective strategy for the monitoring of microplastics in complex aquatic matrices: Exploiting the potential of near infrared hyperspectral imaging (NIR-HSI)
AI summary Read the abstract
Researchers developed a near infrared hyperspectral imaging (NIR-HSI) method for rapid monitoring of microplastics in complex marine matrices, demonstrating effective detection and polymer identification that overcomes the time and cost limitations of conventional spectroscopic analysis approaches.
Microplastics characterization by hyperspectral imaging in the SWIR range
AI summary Read the abstract
Researchers developed a hyperspectral imaging methodology operating in the short-wave infrared range (1000-2500 nm) combined with chemometric classification to rapidly identify polymer types in microplastic samples collected from marine environments. The non-destructive approach enabled polymer characterisation across samples from multiple geographical regions without requiring chemical pre-treatment.
How to identify colorless microplastic directly in the beach sand in a few minutes?
AI summary Read the abstract
Researchers developed a high-throughput near-infrared hyperspectral imaging technique to automatically detect and identify colorless microplastics directly in beach sand within a few minutes. The chemometric classification model can identify multiple plastic polymer types without requiring chemical pre-treatment.
Hyperspectral imaging systems (HSI) and chemometric methods for the rapid and direct detection of microplastics
AI summary Read the abstract
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.
Research digests by email
When a large batch of papers lands in the Atlas, we read through it and send a short write-up of what stood out.