文献类型：期刊文献

英文题名：Machine learning-based in silico quantification framework for non-targeted PFAS in complex vegetable matrices

作者：Ye, Beibei[1];Wang, Jiaxi[1,2];Zhen, Huajun[1,2];Kuang, Jiangmeng[3];Ji, Feng[4];Liu, Qing[5];Yu, Xia[1,2];Zhao, Wentao[6];Sui, Qian[1,2]

机构：[1]East China Univ Sci & Technol, Sch Resources & Environm Engn, Key Lab Environm Risk Assessment & Control Chem Pr, Minist Ecol & Environm, Shanghai 200237, Peoples R China;[2]Shanghai Inst Pollut Control & Ecol Secur, Shanghai 200092, Peoples R China;[3]Thermo Fisher Sci, Shanghai 200120, Peoples R China;[4]Shimadzu China Co Ltd, Beijing 100020, Peoples R China;[5]AB Sciex Analyt Instrument Trading Co Ltd, Shanghai 200335, Peoples R China;[6]Tongji Univ, Coll Environm Sci & Engn, State Key Lab Water Pollut Control & Green Resourc, Shanghai 200092, Peoples R China

年份：2026

卷号：1785

外文期刊名：JOURNAL OF CHROMATOGRAPHY A

收录：;EI(收录号：20263221247347);Scopus(收录号：2-s2.0-105046358739);WOS:【SCI-EXPANDED(收录号:WOS:001843440500001)】；

基金：This research was partly supported by the Fundamental and Inter-disciplinary Disciplines Breakthrough Plan of the Ministry of Education of China (JYB2025XDXM903) , the National Key R & D Program of China (2023YFC3711600) , the National Natural Science Foundation of China (22376066, 22506050) , the Science and Technology Commission of Shanghai Municipality's Yangfan Special Project (23YF1408400) , and the Fundamental Research Funds for the Central Universities. The au-thors thank the Research Center of Analysis and Test of East China University of Science and Technology for their help with the characterization.

语种：英文

外文关键词：Per; and polyfluoroalkyl acid substances; High-resolution mass spectrometry; Complex matrix; Prediction; Orbitrap

摘要：High-resolution mass spectrometry (HRMS) is a powerful tool for the comprehensive identification of per-and polyfluoroalkyl substances (PFAS). However, the lack of reference standards and interference from complex matrices (such as vegetables) pose significant challenges for accurate quantification of non-targeted PFAS, hindering the assessment of their environmental fate and exposure to humans. Therefore, the present study established a machine learning combined with internal standards (ML-IS) model on HRMS instruments to achieve accurate quantification of PFAS in Shanghai cabbage (Brassica chinensis) and white radish (Raphanus sativus) matrices. The optimized ML-IS model achieved excellent predictive performance for non-targeted PFAS concentrations, representing a 1.5-to 8.7-fold improvement in prediction accuracy compared to the model without internal standards. Furthermore, the proposed ML-IS model substantially outperformed both the conventional semi-quantitative method based on structural similarity and the previously published ML model, while also maintaining satisfactory predictive performance across PFAS datasets from other HRMS platforms. Moreover, the ML-IS model was successfully applied to quantify six suspect PFAS in real vegetable samples. These results mark an important step towards advancing HRMS from a tool for qualitative detection to one capable of robust concentration predictions for unknown PFAS in complex vegetable matrices.