农药残留检测中表面增强拉曼光谱的研究进展

农药直接污染环境和食物，最终被人体吸收。其残留物具有高毒性，对人体健康造成严重影响。色谱法、气液色谱串联质谱法等在农药残留检测中应用较为广泛，但存在预处理步骤复杂、费时耗力等缺点。表面增强拉曼光谱（SERS）技术因具备灵敏度高、特异性好、提供全面指纹信息且对样品无损等优点被视为一种新型农残检测方法，可通过简单提取实现液体或固体样品中痕量农药残留的高效检测。在这篇综述中，主要从SERS的增强基底制备、检测方法以及光谱智能解析三个方面对农药残留SERS检测技术及方法的研究进展进行综述，以期为农药残留检测方法提供新的参考。首先，针对SERS增强基底制备，单一的贵金属溶胶纳米颗粒因其“热点”随机、不可控等因素导致稳定性和灵敏性较差，已不能满足痕量农药残留检测。为提高SERS基底的吸附能力使待测物在其表面富集且信号不发生显著变化，对单一贵金属溶胶纳米颗粒进行组装，或加入化学物质、惰性材料等进行修饰制备均一性高的SERS复合基底，保证SERS信号有良好的重现性和灵敏性。其次，为了实现特异性和高灵敏检测，SERS检测方法不再只以单纯的金、银纳米颗粒作为增强基底，而是逐渐趋向于优化样本前处理技术、化学修饰法制备特异性SERS探针、基底物理结构突破以及动态SERS（D-SERS）检测等方向发展。在获得物质的拉曼光谱后，有效拉曼特征区通常在较短的波数范围内，而光谱数据高达上千维，冗余较多，导致后续分析复杂度增加。SERS光谱智能分析则采用化学计量学方法对原始光谱进行预处理、特征提取和模型构建，实现数据降维和主要信息提取，进而实现农残的定性与定量。综上，SERS作为一种快速检测农药残留的方法具有很好的发展前景，可为今后的分析检测领域提供新的借鉴。

Research Progress of Surface-Enhanced Raman Spectroscopy in Pesticide Residue Detection

Pesticides directly pollute the environment and contaminate foods, ultimately being absorbed by the human body. Its residues are highly toxic, which have serious effects on human health. Some methods such as chromatography and gas/liquid chromatography-mass spectrometry have been widely used to detect pesticide residues. However, these methods also have some disadvantages, such as complicated pre-processing steps, time-consuming and labor-intensive. Surface-enhanced Raman spectroscopy (SERS) technology is regarded as a new pesticide residue detection method due to its high sensitivity, good specificity, comprehensive fingerprint information and no damage to the sample. It can realize trace pesticides in liquid or solid samples through simple extraction. In this review, to provide new references in the detection of pesticide residues, we mainly summarized the research progress of SERS detection technology and methods for pesticide residues from the three aspects of the preparation of SERS active substrates, detection methods, and intelligent analysis of spectra. In preparing SERS active substrates, single noble metal sol nanoparticles have poor stability and sensitivity due to random and uncontrollable “hot spots”, which can no longer satisfy trace pesticide residue detection. In order to improve the adsorption capacity of the SERS substrate more target analytes are enriched on the surface of the SERS substrate and the signal does not change significantly. The single noble metal sol nanoparticles are assembled, or its surface is modified by adding chemicals, inert materials, etc., to prepare uniform SERS composite substrate, thereby effectively and specifically capturing the analyte, ensuring good reproducibility and sensitivity of SERS signal. On this basis, in order to achieve the specificity and high sensitivity detection, the detection method of SERS for pesticide residues has gradually evolved from the use of simple nanoparticles such as gold and silver nanoparticles as an enhanced substrate to the optimization of sample pretreatment techniques, the preparation of specific SERS probes by chemical modification, breakthroughs in the physical structure of enhanced substrates, and dynamic SERS(D-SERS) detection. After obtaining the Raman spectrum of the substance, the effective Raman characteristic region is usually within a short wavenumber range, and the spectral data is as high as thousands of dimensions. There is more redundancy, which leads to an increase in the complexity of subsequent analysis. SERS spectrum intelligence analysis often uses chemometrics methods to pre-process the original spectrum, extract features and modeling, realize data dimensionality reduction and main information extraction, and then achieve qualitative and quantitative for pesticide residues. In order to obtain global features and large-scale process data, deep learning methods have also been introduced into SERS spectral intelligent analysis in recent years, which has achieved good analysis results. In summary, SERS has an excellent development prospect for rapid detection of pesticide residues and can provide new ideas for future analysis and testing field.