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Hand-portable HPLC with broadband spectral detection enables analysis of complex polycyclic aromatic hydrocarbon mixtures

Polycyclic aromatic hydrocarbons (PAHs) are considered priority hazardous substances due to their carcinogenic activity and risk to public health. Strict regulations are in place limiting their release into the environment, but enforcement is hampered by a lack of adequate field-testing procedure, i...

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Detalles Bibliográficos
Autores principales: Chatzimichail, Stelios, Rahimi, Faraz, Saifuddin, Aliyah, Surman, Andrew J., Taylor-Robinson, Simon D., Salehi-Reyhani, Ali
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9814556/
https://www.ncbi.nlm.nih.gov/pubmed/36697529
http://dx.doi.org/10.1038/s42004-021-00457-7
Descripción
Sumario:Polycyclic aromatic hydrocarbons (PAHs) are considered priority hazardous substances due to their carcinogenic activity and risk to public health. Strict regulations are in place limiting their release into the environment, but enforcement is hampered by a lack of adequate field-testing procedure, instead relying on sending samples to centralised analytical facilities. Reliably monitoring levels of PAHs in the field is a challenge, owing to the lack of field-deployable analytical methods able to separate, identify, and quantify the complex mixtures in which PAHs are typically observed. Here, we report the development of a hand-portable system based on high-performance liquid chromatography incorporating a spectrally wide absorption detector, capable of fingerprinting PAHs based on their characteristic spectral absorption profiles: identifying 100% of the 24 PAHs tested, including full coverage of the United States Environmental Protection Agency priority pollutant list. We report unsupervised methods to exploit these new capabilities for feature detection and identification, robust enough to detect and classify co-eluting and hidden peaks. Identification is fully independent of their characteristic retention times, mitigating matrix effects which can preclude reliable determination of these analytes in challenging samples. We anticipate the platform to enable more sophisticated analytical measurements, supporting real-time decision making in the field.