基于BPL-SERS法同时多参数多组分快检食品中多种工业染料的研究

The multi-component simultaneously on-site fast detection of trace pollutants is an important issue for the Food Safety Supervision Department to solve. The project plans to study on simultaneously multi-parameter and multi-component fast detection of various industrial dyes in food based on BPL-SERS method. Ten industrial dyes, such as basic oranges and sudan dyes, mentioned in National Supervision Manual on Food Pollutants and Hazardous Materials in 2016, will be studied. Novel SERS substrates are designed to make various types of industrial dyes simultaneously absorb on the SERS substrates due to molecular imprinting and chemical derivation and moreover to obtain SERS spectrum of multi-parameter and multi-component mixture, and then the qualitative and quantitative information of these target molecules is going to be directly obtained via BPL. The key scientific problem of this project is how to obtain high-quality SERS spectra of various multiple trace pollutants simultaneously and how to analyze multi-parameters, such as Raman shift, Raman intensity, relative intensity of Raman bands and intermolecular interaction, simultaneously. The innovation of this project is the realization of one-to-multiple specific adsorption, which is a breakthrough for the traditional SERS substrates of one-to-one specific adsorption. The simultaneously multi-parameter and multi-component fast detection method will provide theoretical basis and technical support for the on-site fast detection for food safety.

微量污染物快速、现场、多组分同时检测是食品安监部门亟待解决的重大问题。本项目拟开展利用贝叶斯程序学习(BPL)与表面增强拉曼散射(SERS)结合，进行食品中多种工业染料快速同时检测的研究。以2016年国家食品污染物和有害因素风险监控工作手册中提及的碱性橙、苏丹红等十种工业染料为研究对象。合成新型SERS基底，使得多种工业染料通过分子印迹或化学衍生法同时特异吸附在SERS基底上并获取多组分混合物的SERS光谱，再利用基于BPL方法直接从中获取这些目标分子的定性、定量信息。项目的关键科学问题是如何同时获取多组分微量污染物的高质量SERS谱图及进行拉曼位移、拉曼峰强、多拉曼峰之间的相对强度、分子间相互作用等多种参数的同时分析。项目的创新之处在于突破了SERS基底“一对一”特异吸附的限制，实现“一对多”特异吸附。这种多参数、多组分同时快速检测的方法可以为食品安全现场快速检测提供理论依据和技术支持。

项目背景：食品是典型的快消品，流通量巨大且直接关系民生，同时，不同食品都有各自的货架期，因此，微量污染物快速、现场、多组分同时快筛是食品安监部门解决巨大流通量与检测能力有限的重要途径之一。主要研究内容：本项目开展的利用贝叶斯程序学习(BPL)与表面增强拉曼散射(SERS)结合，进行食品中多种工业染料快速同时检测的研究，是将计算机算法与检测方法结合的初步探索。本项目以《2016年国家食品污染物和有害因素风险监控工作手册》中提及的碱性橙、罗丹明、苏丹红等十种工业染料为研究对象，设计新型SERS基底使得多种工业染料同时特异吸附在SERS基底上并获取多组分混合物的SERS光谱，再利用基于BPL方法直接从中获取这些目标分子的定性、定量信息。重要结果：在本项目的支持下制备了同时特异吸附多种工业染料分子的SERS基底，对上述十种工业染料进行了谱峰进行了解析。关键数据：通过对比实验谱和理论谱图，获取了高质量SERS谱图，包括拉曼位移、拉曼峰强、多拉曼峰之间的相对强度、电荷转移、分子间相互作用等多种参数。科学意义：本项目意义在于初步探索了同时检测多种目标分子的SERS方法，突破SERS基底“一对一”特异吸附的限制，实现“一对多”特异吸附。这种多参数、多组分同时快速检测的方法可以为食品安全现场快速检测提供理论依据和技术支持。