摘要
Background Bisphenols, as ubiquitous environmental contaminants, pose significant public health concerns owing to their endocrine-disrupting properties. While liquid chromatography-tandem mass spectrometry is the gold standard for their trace-level quantification in biological matrices, its accuracy is fundamentally limited by substantial matrix effects. The development of separation techniques and analytical method for human biomonitoring will significantly advance human health risk assessment. The complexity of biological matrices and low concentrations of targets are the main challenges for human biomonitoring. Developing accessible and economic SPE sorbents suitable for the cleanup of serum is pivotal for conducting large-scale epidemiological and toxicological studies of BPs. Results We present a cleanup protocol based on a 13X-type Na 86 (AlO 2 ) 86 (SiO 2 ) 106 zeolite, and it achieved consistent and acceptable matrix effect suppression (63.3%–121%), outperforming commercial alternatives like C 18 (15.0%–107%), HLB (4.5%–87.7%), and PEP-2 (8.9%–142%). Correlation analysis suggested that analyte recovery was significantly associated with logK ow ( P < 0.01) and positively correlated with estimated molecular volume. Combining the binding energy analysis, our finding suggested the role of shape selectivity and strong interaction (e.g. hydrogen bond interaction, electrostatic interaction) within the zeolite pores. The developed method was validated with bovine serum, demonstrating acceptable sensitivity (0.075–0.39 ng mL −1 ), precision (2.2%–18.0%), and accuracy (71.4%–124%). Its reliability for human biomonitoring was further evaluated by the good agreement with a reference method via Bland-Altman analysis. Significance The zeolite-based SPE protocol demonstrates good selectivity and robustness, establishing it as a superior sample preparation technique for the biomonitoring of BPs in serum. It is feasible for the accurate assessment of the human internal exposure to BPs. In addition, our work identifies critical factors influencing extraction efficiency and molecular interactions via multiple regression analysis and molecular simulation, thereby providing a possible mechanistic insight.
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