[Development of a machine learning-based ionization efficiency prediction model for per- and polyfluoroalkyl substances and its application in semi-quantitative analysis].

Per- and polyfluoroalkyl substances （PFASs） represent a category of emerging contaminants of global concern in fields such as environmental science and food safety， due to their persistence， bioaccumulative properties and potential toxicity. Although screening methods for PFASs using high resolution mass spectrometry （HRMS） have been developed rapidly， the diversity of PFASs and the absence of standards pose significant challenges for quantitative analysis. In this study， 50 PFASs were analyzed by HPLC-HRMS. The ionization efficiency （IE） was calculated as the slope of the calibration curve. A quantitative structure-activity relationship （QSAR） model was developed employing machine learning to predict the ionization efficiencies of PFASs using PaDEL molecular descriptors. The model enables semi-quantitative estimation of PFASs concentrations in the absence of reference standards by incorporating predicted IE values. Eighteen critical descriptors were selected from a total of 1 444 PaDEL descriptors through the application of recursive feature elimination （RFE）. These selected descriptors encompassed topological descriptors， geometrical descriptors， autocorrelation descriptors， electrostatic and polarity descriptors. These individual descriptors including VE1_Dzv， GATS6i， JGI10， GATS1p and MATS4m were of great importance. Three algorithms including elastic net linear regression， random forest （RF）， and XGBoost were evaluated for model performance. In the elastic net linear regression model， the root mean square error （RMSE） for the training dataset was 0.049 0， and the coefficient of determination （