【作者】 张茜； 王晓彬； 黄双根； 刘木华；

【Author】 ZHANG Xi;WANG Xiao-bin;HUANG Shuang-gen;LIU Mu-hua;Nanchang Normal University;Key Laboratory of Modern Agricultural Equipment,Jiangxi Agricultural University;

【机构】 南昌师范学院； 江西农业大学现代农业装备重点实验室；

【摘要】 基于表面增强拉曼光谱（SERS）技术研究了小白菜中伏杀硫磷农药残留的定量检测方法。采用QuEchERS方法实现小白菜中伏杀硫磷农药的提取和基质成分的去除,以金纳米颗粒为增强基底,获取小白菜中不同浓度伏杀硫磷农药残留的SERS信号,应用偏最小二乘法建立定量检测模型。结果显示,小白菜中伏杀硫磷农药的最低检测浓度为0.96 mg·kg^-1;定量模型对预测集样本的均方根误差为1.48,相关系数为0.968 7。模型对未知样本的预测回收率为95.76%～102.78%,相对误差绝对值在5%以下,表明模型具有较好的预测效果,可用于小白菜中伏杀硫磷农药残留的定量检测。更多还原

【Abstract】 The quantitative detection method of pcosalone residues in pakchoi was studied based on surface-enhanced Raman spectroscopy（SERS） in this paper.QuEchERS method was used to extract the phosalone pesticides and remove matrix components from the pakchoi.SERS signals of different concentrations of phosalone residues in pakchoi were obtained on the basis of gold nanoparticles, and the quantitative detection model was established by the partial least square method.The results showed that the lowest detection concentration of phosalone in pakchoi was 0.96 mg·kg^-1.The root-mean-square error of the quantitative model on the samples of the prediction set is 1.48,and the correlation coefficient is 0.968 7.The predicted recovery rate of unknown samples is 95.76%～102.78%,and the relative error is less than 5%,which shows that the model has a good prediction effect and can be used for the quantitative detection of phosalone residues in pakchoi.更多还原