表面增强拉曼光谱检测脐橙果皮混合农药残留

Surface enhanced Raman scattering detection of mixing pesticide residual on orange peel

为了研究果皮农药残留快速检测方法。该文以脐橙为例,混合农药(亚胺硫磷和乐果)为研究对象,选用银纳米线作为增强基底,利用共焦显微拉曼光谱仪对农药残留进行检测。通过表面增强拉曼光谱(surface enhanced Raman scattering,SERS)技术,采集脐橙表皮混合农药残留的SERS光谱。对混合农药定性分析,银纳米线对2种农药都有较好的增强效果。对采集的光谱进行预处理后,建立模型,进行定量分析,研究结果表明,经过二阶微分预处理后光谱数据结合偏最小二乘法(partial least squares,PLS)得到的模型预测效果最好,预测相关系数(R_p)为0.954,其预测均方根误差(root-mean-square prediction error,RMSEP)为4.822 mg/L。挑选两种农药特征峰的特征波段,混合农药中亚胺硫磷的特征波段经多元散射校正(multiplicative scatter correction,MSC)处理后,建模效果较好,其中R_p为0.898,RMSEP为6.621 mg/L;混合农药中乐果的特征波段经基线校正处理后,建模效果较好,其中R_p为0.911,RMSEP为7.369 mg/L。研究结果表明SERS技术是一种快速、可靠的检测混合农药残留的方法。

In recent years, pesticide has been mass-producing and widely used. The problem of pesticide residues has attracted more and more attention. As the problem of food safety is becoming the focus of society, the pesticide residue detection has become a research hotspot. Among numerous methods of pesticide detection, surface-enhanced Raman spectroscopy（SERS） has become an area of intense research owing to a highly sensitive probe for the trace level detection of pesticide. The spectroscopic merits of SERS are the representation in the aspects of super sensitivity, high selection and water resistance, which make it one of the most popular detection techniques currently. In this paper, the organophosphorus pesticide phosmet and dimethoate were selected as the research objects. The blended pesticide residues of phosmet and dimethoate on navel orange were detected by the SERS combined with chemometrics algorithm. The silver nanowires were used as SERS substrate to detecte pesticide residue on navel orange. Firstly, the navel orange samples were fabricated with pesticide residues. Secondly, the silver nanowires SERS substrate was fabricated. Then the sample solution to be measured was dripped onto the dried SERS substrate. When the sample was dried, spectral data were collected. The spectral data were used to analyze pesticide residue qualitatively and quantitatively. It had a better enhancement effect on the qualitative analysis of mixing pesticides for silver nanowires substrate. Pesticide original spectral data were processed by the partial least square（PLS） modeling algorithm and the different pretreatment methods. The PLS regression combined with different data preprocessing methods was used to develop quantitative models of mixing pesticide residue. And the advantages and disadvantages of the models were compared. The results showed that the model built by the PLS combined with the second derivatives data preprocessing was ideal for mixing pesticides, whose correlation coefficient（R_p） for the prediction was 0.954, and root mean square error of prediction（RMSEP） was 4.822 mg/L. The model combined with the baseline was ideal for phosmet, whose R_p was 0.898 and RMSEP was 6.621 mg/L. The model combined with the multiplicative scattering correction（MSC） was ideal for dimethoate, whose R_p was 0.911 and RMSEP was 7.369 mg/L. Therefore, the study combines the SERS and chemometrics algorithm to detect pesticide residues qualitatively and quantitatively, which is feasible. Raman spectroscopy can be used as a fast and simple tool to detecte mixing pesticide residues. It provides a basis for the more insightful study on pesticide residues detection.