食品中未知非法添加物的快速锁定和智能结构解析方法研究

Illegal addition is the main problem in the field of food safety in China, construction of non-targeted screening platform is necessary. But finding and identification of unknown illegal additives are the main challenge. Aiming at this problem, in-house database including retention time and multiple-stage mass spectrometry of 2000 endogenous and exogenous compounds was established. Nontargeted screening strategy was investigated for aquatic product samples including suspect group and control group based on liquid chromatography-mass spectrometry (LC-MS), workflow for illegal additives finding and identification was studied. Based on these results, the key techniques for finding and identification of unknown illegal additives in general foods will be further studied in this project. New and reliable peak matching method and data filtering strategy will be developed, fast screening workflow for risk finding of general animal-derived foods will be established. The comprehensive and intelligent compounds identification strategy will be progressed based on accurate mass, mass spectrum rule, fragmentation tree and retention time prediction. This study will provide the necessary technical support for safety regulation and risk warning of foods.

非法添加是现阶段我国食品安全领域的突出问题，非靶向筛查技术是解决这一问题的关键，但未知非法添加物的快速锁定和结构解析是当前该技术面临的主要挑战。在前期工作中我们构建了2000个内源性及外源性物质的保留时间和多级质谱数据库，并利用有空白对照的水产品“实验组/对照组”建立了基于液相色谱-质谱（LC-MS）的非靶向筛查方法，对非法添加物锁定流程进行了初步探索。在此基础上，本项目拟针对普通（无对照）动物源食品（如肉制品等）研发普适性的未知非法添加物锁定技术，结合数据库自动检索系统实现非法添加物的快速锁定；针对未知物定性难题，拟构建质谱数据智能结构解析系统，结合高分辨质谱精确质量及裂解规律、碎片树子结构、保留时间预测实现未知非法添加物的智能结构解析。预计通过本项目的实施，可突破食品中非法添加物非靶向筛查的技术难点，为食品安全性评价和风险预警提供必要的技术手段和支持。

食品安全关系国计民生。本项目针对食品中未知非法添加物质的非靶向筛查技术需求，构建了包括样品快速预处理、色谱快梯度分离和高分辨质谱高灵敏检测的标准化分析方法。针对无空白对照的风险食品样品，通过样品内源性物质去除、均值偏差倍率计算、风险物质质谱裂解特征解析，再结合自编智能检索程序IdentifyClass，构建了潜在风险物质的智能检索系统，所建方法在混合标准样品体系的分析中可以达到100%的分析准确率。此外，还建立了基于质谱碎裂规律、保留时间预测及多数据库检索相结合的未知物定性策略。以上方法已经用于肉类样品未知风险物质的非靶向筛查中，为食品安全性评价和风险预警提供了重要的技术手段和支持。

内容获取失败，请点击重试