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Rapid Quantitative Analysis of Dimethoate Pesticide Using Surface-Enhanced Raman Spectroscopy

机译:使用表面增强拉曼光谱快速定量分析乐果农药

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Pesticides are widely used in agriculture, and pesticide residues have become a public concern. So far, no analytical method has been available for the rapid and quantitative analysis of most food pesticides. In this study, the application of micro-surface-enhanced Raman spectroscopy (SERS) for analysis of the typical organophosphorous pesticide dimethoate is demonstrated. Huge enhanced Raman signals of pesticides at low concentrations of 0.5 to 10 mug mL~(-1) were acquired by confocal Raman micro-spectrometry with Klarite substrate. The observed spectra were analyzed by comparison with the normal Raman spectra of dimethoate. Partial least squares (PLS) regression combined with different data preprocessing methods and wavelength selection was applied to develop quantitative models for dimethoate solutions. The best model, with the highest correlation coefficient (0.969) and the lowest root mean square error of predictions (0.626), was achieved with the first derivative combined with standard normalized variate (SNV) spectra and the wavelength bands of 1845.5±1186.9 cm~(-1) and 1023±199.5 cm~(-1). This study indicated that SERS coupled with a nanosubstrate is a potential tool for rapid quantification ofpesticide residues at 10~6 concentration levels.
机译:农药广泛用于农业,农药残留已成为公众关注的问题。到目前为止,还没有一种分析方法可用于大多数食品农药的快速定量分析。在这项研究中,证明了微表面增强拉曼光谱(SERS)在分析典型有机磷农药乐果中的应用。通过与Klarite底物的共聚焦拉曼光谱分析,获得了0.5至10杯mL〜(-1)低浓度农药的巨大增强拉曼信号。通过与乐果的正常拉曼光谱比较来分析观察到的光谱。偏最小二乘(PLS)回归结合不同的数据预处理方法和波长选择被用于开发乐果溶液的定量模型。一阶导数结合标准归一化变量(SNV)光谱和1845.5±1186.9 cm〜1的波长带可以得到最佳的模型,具有最高的相关系数(0.969)和最低的预测均方根误差(0.626)〜 (-1)和1023±199.5 cm〜(-1)。这项研究表明,SERS结合纳米底物是快速定量10〜6浓度农药残留的潜在工具。

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