Construction of hollow fiber stirring bar based on dummy molecularly imprinted polymers for the selective determination of organophosphorus pesticides in food samples

In this study, the sol-gel method and layer-by-layer self-assembly technology were used to graft dummy template molecular imprinting polymers (DMIPs) onto the surface of microspheres (CG161M), thereby preparing the DMIPs@CG161M composite material. This composite material was then utilized as an adsorbent for detecting organophosphorus pesticides in food. A structurally analogous yet less toxic compound, diphenyl chlorophosphate, was employed as the dummy template to circumvent the detection interference arising from template leakage-a common drawback of traditional molecularly imprinted polymers-while also minimizing the safety risks associated with handling highly toxic pesticide standards during experimentation. The DMIPs@CG161M composite was immobilized as an adsorption medium on a hollow fiber stirring bar (HF-SBSE) to develop a solid-phase microextraction adsorbent. Combined with gas chromatography-mass spectrometry (GC-MS), a highly sensitive analytical method was established for detecting organophosphorus pesticides in food samples. Under optimal conditions, the method demonstrated good linearity for five organophosphorus pesticides- diazinon, parathion-methyl, fenitrothion, chlorpyrifos, and parathion-across a concentration range of 0.01-10 mg/L, with determination coefficients (R²) exceeding 0.9998. The limits of detection (LOD) and quantification (LOQ) ranged from 0.01 to 0.03 mg/L and 0.03 to 0.11 mg/L, respectively. In the spiked food sample analysis, the recovery rates fell within the range of 61.61% to 91.41%, with relative standard deviations (RSD) recorded between 4.01% and 6.91%. These findings indicate that the synthesized DMIPs@CG161M composite exhibits excellent selective adsorption performance toward organophosphorus pesticides in food samples, highlighting its strong potential as an efficient adsorbent for practical applications.