邻苯二甲酸盐
固相微萃取
吸附
化学
邻苯二甲酸
相(物质)
色谱法
化学工程
有机化学
气相色谱-质谱法
质谱法
工程类
作者
Shuo Liu,Yahuan Li,Xiaohuan Zang,Qingyun Chang,Shuaihua Zhang,Zhi Wang,Zhi Wang
标识
DOI:10.1016/j.microc.2024.110446
摘要
Phthalate esters (PAEs), as widely used additives in manufacturing plastic materials, can migrate from plastic packages into food and cause adverse effects on human health. In this work, a novel MXene-based adsorbent (named PDBA-MXene) was prepared by a simple solvothermal reaction with 1,4-phenylenediboronic acid (PDBA) as an intercalating agent for MXene. Compared with pristine MXene, PDBA-MXene presented a better adsorption performance. It could be attributed to the insertion of the PDBA which can reduce the self-stacking of the MXene and widen the interlayer spacing, thus facilitating the exposure of the available active sites and further enhancing the adsorption performance. PDBA-MXene was utilized as a coating material for solid-phase microextraction (SPME) fibers, aimed at the extraction and subsequent determination of some PAEs in jam samples. The fabricated fiber demonstrated a strong adsorption capability and selectivity for PAEs. By combining the SPME with gas chromatography-flame ionization detection (GC-FID), a trace analysis method for the quantification of nine PAEs in jams has been established. The main parameters for the SPME were optimized using the response surface methodology based on central composite design. Under optimized conditions, a good linear response was obtained for the analytes in the range of 0.33–180 ng g−1 with the determination coefficient (r2) values from 0.9951 to 0.9978. The limits of detection (LODs) for the analytes were between 0.10 ng g−1 and 6.0 ng g−1. The method recoveries for the PAEs from spiked jam samples ranged from 75.3 % to 125 %, and the relative standard deviations (n = 5) varied from 1.8 % to 12 %. The optimized SPME-GC-FID method illustrated excellent performance in terms of sensitivity, reproducibility, and accuracy, thereby presenting a good alternative approach for the routine monitoring of PAEs in complex food matrices like jam.
科研通智能强力驱动
Strongly Powered by AbleSci AI