Practical Semiquantification Strategy for Estimating Suspect Per- and Polyfluoroalkyl Substance (PFAS) Concentrations
By Dunping Cao, Trever Schwichtenberg, Chenyang Duan, Lan Xue, Derek Muensterman, and Jennifer Field
J Am Soc Mass Spectrom
April 5, 2023
DOI: 10.1021/jasms.3c00019
Semiquantitation of suspect per- and polyfluoroalkyl substances (PFAS) in complex mixtures is challenging due to the increasing number of suspect PFAS. Traditional 1:1 matching strategies require selecting calibrants (target-surrogate standard pairs) based on head group, fluorinated chain length, and retention time, which is time-consuming and requires expert knowledge. Lack of uniformity in calibrant selection for estimating suspect concentrations among different laboratories makes comparing reported suspect concentrations difficult. In this study, a practical approach whereby the area counts for 50 anionic and 5 zwitterionic/cationic target PFAS were ratioed to the average area of their respective stable-isotope labeled surrogates to create "average PFAS calibration curves" for suspects detected in negative- and positive-ionization mode liquid chromatography quadrupole time-of-flight mass spectrometry. The calibration curves were fitted with log-log and weighted linear regression models. The two models were evaluated for their accuracy and prediction interval in predicting the target PFAS concentrations. The average PFAS calibration curves were then used to estimate the suspect PFAS concentration in a well-characterized aqueous film-forming foam. Weighted linear regression resulted in more target PFAS that fell within 70-130% of their known standard value and narrower prediction intervals than the log-log transformation approach. The summed suspect PFAS concentrations calculated by weighted linear regression and log-log transformation were within 8 and 16% of those estimated by a 1:1 matching strategy. The average PFAS calibration curve can be easily expanded and can be applied to any suspect PFAS even if the confidence in the suspect structure is low or unknown.
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