采用超高效液相色谱-串联质谱（UPLC-MS/MS）技术，建立了人体摄入吸收后的食物致癌物杂环胺（HAAs）与血清白蛋白（HSA）形成的亚磺酰胺加合物的准确、高灵敏度检测方法，用于评估HAAs的摄取，研究HAAs与相关癌症风险的关系。选取2-氨基-1-甲基-6-苯基咪唑并[4,5-b]吡啶（PhIP）为研究对象，依据PhIP在人体内的代谢途径，体外合成PhIP-HSA亚磺酰胺加合物。使用UPLC-MS/MS检测PhIP-HSA亚磺酰胺加合物的酸水解产物PhIP，对PhIP-HSA亚磺酰胺加合物进行定量。结果表明，HiTrap Blue亲和柱对HSA的回收率高于90%；优化的条件下，PhIP-HSA亚磺酰胺加合物的水解效率高达96%；PhIP-HSA亚磺酰胺加合物的含量与PhIP的信号强度呈良好线性关系；线性回归方程是y=1.011 7x+3.256 3，R²=0.998 7。该方法的检测限和定量限分别低至5×10-3 fmol/mg HAS和1.50×10-3 fmol/mg HSA。该方法对人血浆中PhIP-HSA亚磺酰胺加合物的测定具有较高的灵敏度，在HAAs摄入与相关癌症风险关系的研究中，是用于评估常食用熟肉个体的HAAs摄入的可行方法。

An accurate and highly sensitive assay was developed for the determination of sulfinamide adducts formed by heterocyclic aromatic amines (HAAs) and human serum albumin (HSA) after human ingestion and absorption, using ultra-performance liquid chromatography tandem mass spectrometry (UPLC-MS/MS). The aim of this study was to assess exposure to HAAs, and further investigate the related cancer risks. Using 2-amino-1-methyl-6-phenylimidazo [4,5-b] pyridine (PhIP) as a research subject, the PhIP-HSA sulfinamide adduct was synthesized in vitro according to the metabolic pathway of the human body and was quantified using UPLC-MS/MS based on the detection of PhIP derived from its acid hydrolysis. The results showed that the recovery of HSA by the HiTrap Blue affinity columns was greater than 90%, and the hydrolysis efficiency of the PhIP-HSA sulfinamide adduct reached 96% under optimized conditions. There was a positive linear relationship between the PhIP-HSA sulfinamide adduct content and the signal intensity of PhIP, and the linear regression equation was y=1.011 7x+3.256 3, R²=0.998 7. The respective detection and quantitation limits for the PhIP-HSA sulfinamide adduct in plasma were as low as 5×10-3 fmol/mg HSA and 1.50×10-3 fmol/mg HSA. This method showed a high degree of sensitivity when determining PhIP-HSA sulfinamide adducts in human plasma, and proved to be a feasible method for the evaluation of exposure to HAAs in individuals who frequently eat cooked meat, as well as the associated cancer risks.

国家自然科学基金项目（21405118）