@article {TANG2022,
	title = {Quantification of perfluorinated compounds in atmospheric particulate shows potential connection with environmental event},
	journal = {Journal of Environmental Sciences},
	volume = {136},
	year = {2024},
	month = {02/2024},
	pages = {237-247},
	type = {Research},
	chapter = {237},
	abstract = {<p>A method of quantification of perfluorinated compounds (PFCs) from atmospheric particulate matter (APM) is described. A single step pretreatment method, selective pressurized liquid extraction (SPLE), was developed to reduce the high matrix background and avoid contamination from commonly used multiple sample pretreatment steps. An effective sorbent was selected to purify the PFCs during SPLE, followed by liquid chromatography-tandem mass spectrometry (LC\&ndash;MS/MS), for quantification of PFCs. Conditions affecting the SPLE efficiency, including temperature, static extraction time, and number of extraction cycles used, were studied. The optimum conditions were found to be 120\&deg;C, 10 min, and 3 cycles, respectively. LC-MS/MS method was developed to obtain the optimal sensitivity specific to PFCs. The method detection limits (MDLs) were 0.006 to 0.48 ng/g for the PFCs studied and the linear response range was from 0.1 to 100 ng/g. To ensure accurate values were obtained, each step of the experiment was evaluated and controlled to prevent contamination. The optimized method was tested by performing spiking experiments in natural particulate matter matrices and good rates of recovery and reproducibility were obtained for all target compounds. Finally, the method was successfully used to measure 16 PFCs in the APM samples collected in Beijing over five years from 2015 to 2019. It is observed that some PFCs follow the trend of total PFC changes, and can be attributed to the environment influencing events and policy enforcement, while others don\&$\#$39;t seem to change as much with time of the year or from year to year.</p>},
	keywords = {Atmospheric particulate matter, Liquid chromatography-mass spectrometry, Perfluorinated compounds, Pollution control, Selective pressurized liquid extraction},
	issn = {1001-0742},
	doi = {https://doi.org/10.1016/j.jes.2022.08.036},
	url = {https://www.sciencedirect.com/science/article/pii/S1001074222004387},
	author = {Hua Tang and Ying Wang and Shengling Si and Hongli Li and Chen, D. D. Y.}
}
@article {GE2020121880,
	title = {Data-driven development of liquid chromatography-mass spectrometry methods for combined sample matrices},
	journal = {Talanta},
	volume = {224},
	year = {2021},
	pages = {121880},
	abstract = {<p>Herbal medicine formulas (HMFs), the combinations of two or more herbal medicine (HM) ingredients required in a single prescription, are a typical kind of combined sample matrices. LC-MS is a powerful platform for the analyses of such complex samples. The optimization of separation conditions may require a lot of experiments, because multiple analytes need to be separated from a plethora of possible interfering compounds in the sample mixture containing different herbal medicines. To greatly reduce the complexity needed for the optimization of separation conditions, this work proposes a data-driven approach for the systematic development of LC-MS methods for HMFs, using six HMFs created from four HMs (Atractylodis Macrocephalae Rhizoma, Paeoniae Radix Alba, Corydalis Rhizoma and Ophiopogonis Radix) as case-studies. In this approach, the chromatographic peak parameters (like retention times) of the analytes and interfering compounds under different separation conditions were extracted from the LC-MS database of the HMs. Then data-driven models between the chromatographic peak parameters and the separation parameters were built with machine learning methods (r \&gt; 0.996 for all the compounds) and used to predict the chromatographic peaks of the analytes and interfering compounds in HMF analyses. Based on the predictions, all of the separation parameters were optimized without any previous experiments on the HMFs. In the validation experiments for the six HMFs, all of the analytes were well separated. The data-driven approach demonstrated enables systematic and rapid development of LC-MS methods for HMFs, and the separation conditions can be efficiently adjusted for different analytes.</p>},
	keywords = {Artificial neural network, Data-driven, Herbal medicine formula, Least squares support vector machine, Liquid chromatography-mass spectrometry, MONTE-CARLO SIMULATION},
	issn = {0039-9140},
	doi = {https://doi.org/10.1016/j.talanta.2020.121880},
	url = {http://www.sciencedirect.com/science/article/pii/S0039914020311711},
	author = {Zhiwei Ge and Kuanyong Zhang and David D. Y. Chen and Binjun Yan}
}
