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首页> 美国卫生研究院文献>Sensors (Basel Switzerland) >Rapid Detection of Pesticide Residues in Paddy Water Using Surface-Enhanced Raman Spectroscopy
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Rapid Detection of Pesticide Residues in Paddy Water Using Surface-Enhanced Raman Spectroscopy

机译：表面增强拉曼光谱法快速检测稻田水中的农药残留

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Pesticide residue in paddy water is one of the main factors affecting the quality and safety of rice, however, the negative effect of this residue can be effectively prevented and reduced through early detection. This study developed a rapid detection method for fonofos, phosmet, and sulfoxaflor in paddy water through chemometric methods and surface-enhanced Raman spectroscopy (SERS). Residue from paddy water samples was directly used for SERS measurement. The obtained spectra from the SERS can detect 0.5 mg/L fonofos, 0.25 mg/L phosmet, and 1 mg/L sulfoxaflor through the appearance of major characteristic peaks. Then, we used chemometric methods to develop models for the intelligent analysis of pesticides, alongside the SERS spectra. The classification models developed by K-nearest neighbor identified all of the samples, with an accuracy of 100%. For the quantitative analysis, the partial least squares regression models obtained the best predicted performance for fonofos and sulfoxaflor, and the support vector machine model provided optimal results, with a root-mean-square error of validation of 0.207 and a coefficient of determination of validation of 0.99952, for phosmet. Experiments for actual contaminated samples also showed that the above models predicted the pesticide residue values with high accuracy. Overall, using SERS with chemometric methods provided a simple and convenient approach for the detection of pesticide residues in paddy water.
机译：稻田水中的农药残留是影响稻米质量和安全性的主要因素之一，但是可以通过早期发现有效地预防和减少这种残留的负面影响。这项研究开发了一种快速检测方法，通过化学计量学方法和表面增强拉曼光谱法（SERS）对稻田中的fonofos，phosmet和sulfoxaflor进行了检测。稻谷水样品中的残留物直接用于SERS测定。通过出现主要特征峰，从SERS获得的光谱可以检测出0.5 mg / L的fonofos，0.25 mg / L的次磷酸盐和1 mg / L的硫代草花。然后，我们使用化学计量学方法开发了用于智能分析农药的模型以及SERS光谱。由K近邻开发的分类模型可识别所有样本，准确度为100％。对于定量分析，偏最小二乘回归模型获得了fonofos和sulfoxaflor的最佳预测性能，而支持向量机模型提供了最佳结果，验证的均方根误差为0.207，验证的确定系数为0.99952，适用于phosmet。实际污染样品的实验还表明，以上模型可以高精度预测农药残留量。总体而言，将SERS与化学计量学方法结合使用可提供一种简便的方法来检测稻田中的农药残留。

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