Non-targeted screening of emerging contaminants in drinking water based on high-resolution mass spectrometry / 上海预防医学

ObjectiveTo establish a non-targeted screening method for emerging contaminants in drinking water based on high-resolution mass spectrometry and apply it to actual water samples. MethodsA total of 9 drinking water samples collected from 3 reservoirs in Shanghai were purified and concentrated by HLB solid phase extraction column， then separated and analyzed by liquid chromatography high-resolution mass spectrometer and gas chromatography high⁃resolution mass spectrometer. The acquired data were analyzed by Thermo Tracefinder， Excel and other software combined with mzCloud and NIST databases. The methodology was verified with representative compound standards. Pesticide and perfluorinated compounds were taken as examples to analyze their pollution status. ResultsA non-targeted analysis strategy based on liquid chromatography and gas chromatography tandem high-resolution mass spectrometry was established. The pollution level of 20 kinds of pesticides and 4 kinds of perfluorinated compounds identified in 9 drinking water samples were higher in the Huangpu River than in the Yangtze River estuary. ConclusionThe established non-targeted screening method by high-resolution mass spectrometry can detect potential emerging contaminants in drinking water without relying on the standards， which provides a powerful technical means for water quality monitoring and risk assessment.

Research Progress on Machine Learning Assisted Non-Targeted Screening Strategy for Identification of Fentanyl Analogs / 法医学杂志

In recent years, the types and quantities of fentanyl analogs have increased rapidly. It has become a hotspot in the illicit drug control field of how to quickly identify novel fentanyl analogs and to shorten the blank regulatory period. At present, the identification methods of fentanyl analogs that have been developed mostly rely on reference materials to target fentanyl analogs or their metabolites with known chemical structures, but these methods face challenges when analyzing new compounds with unknown structures. In recent years, emerging machine learning technology can quickly and automatically extract valuable features from massive data, which provides inspiration for the non-targeted screening of fentanyl analogs. For example, the wide application of instruments like Raman spectroscopy, nuclear magnetic resonance spectroscopy, high resolution mass spectrometry, and other instruments can maximize the mining of the characteristic data related to fentanyl analogs in samples. Combining this data with an appropriate machine learning model, researchers may create a variety of high-performance non-targeted fentanyl identification methods. This paper reviews the recent research on the application of machine learning assisted non-targeted screening strategy for the identification of fentanyl analogs, and looks forward to the future development trend in this field.