果蔬中农药残留的表面增强拉曼光谱指纹识别及痕量检测研究

The problem of pesticide residue on the fruit and vegetable becomes increasingly serious in recent years, and the key to guarantee food safety is the development of rapid identification and detection technology for pesticide residue. The program aims to establish the method of the fingerprint identification and rapid quantitative detection for representative pesticide residues on fruits and vegetables by means of surface-enhanced Raman spectroscopy (SERS). The relationship between the spectral characteristic of SERS and the properties of pesticide will be investigated, and the mechanism of identification various pesticides by SERS will be also clarified. The methods of extraction and purification of pesticide residues on fruits and vegetables will be studied. Molecular imprinting technique、solid phase microextraction and liquid phase microextraction for sample pretreatment will be compared to select the best method, as well as the simplified pretreatment process will be explore by further experiments and research. Compared of the results of SERS detection based on different sample pretreatments, a new sample pretreatment method will be established for fast and effective determination pesticide residues by SERS. In order to discuss the theory of the surface enhancement effect, the fabrication of surface enhanced raman substrate、performance test and confirmation by way of detection of pesticide residues using SERS will study systemically. As a result, the qualitative and quantitative models will be established for pesticide residues on fruits and vegetables by SERS combining with various chemometric methods, which can achieve rapid detection of pesticide residues. These results can provide a scientific method and basis for application of the SERS technology as a rapid analytic technique in the detection of pesticide residues on fruit and vegetable.

目前国内果蔬农药残留问题日趋严重，危害人类健康，引起社会高度关注，农药残留的快速鉴别与检测技术已成为急需解决的问题。本项目拟以果蔬中常用的具有代表性的农药残留为研究对象，建立基于表面增强拉曼光谱分析技术（SERS）农药残留的指纹识别和快速定量检测方法。采用固相微萃取、液-液微萃取等技术对果蔬中农药残留进行提取、净化，根据SERS检测结果，简化前处理过程，建立快速有效的果蔬农药残留前处理方法；对纳米基底的制备条件及其性能进行系统的研究，探讨拉曼表面增强机理。在此基础上，结合化学计量法，分析SERS光谱特性与农药属性的相互关系，阐明SERS鉴别农药种类的指纹识别机理，建立SERS技术快速检测农药残留的定性定量分析模型。本研究为SERS技术在果蔬中农药残留快速检测分析提供科学方法和依据。

果蔬中农药残留问题已成为主要的食品安全问题之一，引起我国及国际社会的高度关注，因此，农药残留快速鉴别与检测技术的研究和应用迫在眉睫。本研究以苹果中农药残留为对象，利用表面增强拉曼光谱（Surface-enhanced Raman spectroscopy，SERS）分析技术结合化学计量学方法，开展苹果中亚胺硫磷和甲萘威残留痕量检测和分子识别的基础研究，建立了基于SERS技术的农药残留检测方法。实验结果表明苹果中亚胺硫磷和甲萘威的最低检测浓度均为1 ppm，定量分析模型的相关性系数R2分别为0.980和0.985，农药定性判别的正确率高于96.7%。本项目的研究结果表明SERS光谱分析技术在食品痕量化学污染物检测及分子识别中具有极大的应用潜力。