Chatzimichail Stelios, Rahimi Faraz, Saifuddin Aliyah, Surman Andrew J, Taylor-Robinson Simon D, Salehi-Reyhani Ali
Department of Surgery and Cancer, Imperial College London, London, W12 0HS, UK.
Department of Chemistry, King's College London, London, SE1 1DB, UK.
Commun Chem. 2021 Feb 16;4(1):17. doi: 10.1038/s42004-021-00457-7.
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.
多环芳烃(PAHs)因其致癌活性和对公众健康的风险而被视为优先有害物质。目前已有严格的法规限制其向环境中的排放,但由于缺乏足够的现场测试程序,执法工作受到阻碍,目前只能依靠将样品送往集中分析设施进行检测。由于缺乏能够分离、识别和量化通常观察到多环芳烃的复杂混合物的现场可部署分析方法,在现场可靠地监测多环芳烃水平是一项挑战。在此,我们报告了一种基于高效液相色谱的便携式系统的开发,该系统配备了光谱吸收范围宽的检测器,能够根据多环芳烃的特征光谱吸收谱对其进行指纹识别:在所测试的24种多环芳烃中,识别率达100%,包括美国环境保护局优先污染物清单中的所有物质。我们报告了利用这些新功能进行特征检测和识别的无监督方法,这些方法足够稳健,能够检测和分类共洗脱峰和隐藏峰。识别完全独立于它们的特征保留时间,减轻了基质效应,而基质效应可能会妨碍在具有挑战性的样品中可靠地测定这些分析物。我们预计该平台将实现更复杂的分析测量,支持现场实时决策。