Abstract：
BACKGROUND: Chlorpyrifos is a commonly used organophosphorus pesticide in agriculture. However, its neurotoxicity poses a huge threat to human health. In the present study, a chitosan-modified filter paper-based surface enhanced Raman scattering active substrate (Ch/AgNPs/paper) was fabricated and used to detect trace amounts of chlorpyrifos in 120 treated wheat samples.
RESULTS: Results showed that the Ch/AgNPs/paper substrate could be used to enhance the chlorpyrifos spectral fingerprint only up to a concentration of 0.000558 mg L-1 . Following Raman spectra acquisition, three pre-processing methods, including Savitzky-Golay (Savitsky-Golay filter with a second order polynomial) smoothing with first derivative and second derivative and normalization, were used to reduce baseline variation and increase resolutions of spectral peak features of the original spectra dataset. Then, prediction models based on partial least squares were established for detecting chlorpyrifos pesticide residue in wheat. The partial least squares model with normalization yielded optimal result, with a correlation coefficient of 0.9764, root mean square error of prediction of 1.22 mg L-1 in the prediction, and relative analysis deviation of 4.12. Five unknown samples were prepared to verify the accuracy of the prediction model. The predicted recoveries were calculated to be between 97.25% and 119.38% with an absolute t value of 0.598. The value of a t-test shows that the prediction model is accurate and reliable.
CONCLUSIONS: The present study demonstrates that the proposed method can achieve rapid detection of chlorpyrifos in wheat. © 2022 Society of Chemical Industry.
RESULTS: Results showed that the Ch/AgNPs/paper substrate could be used to enhance the chlorpyrifos spectral fingerprint only up to a concentration of 0.000558 mg L-1 . Following Raman spectra acquisition, three pre-processing methods, including Savitzky-Golay (Savitsky-Golay filter with a second order polynomial) smoothing with first derivative and second derivative and normalization, were used to reduce baseline variation and increase resolutions of spectral peak features of the original spectra dataset. Then, prediction models based on partial least squares were established for detecting chlorpyrifos pesticide residue in wheat. The partial least squares model with normalization yielded optimal result, with a correlation coefficient of 0.9764, root mean square error of prediction of 1.22 mg L-1 in the prediction, and relative analysis deviation of 4.12. Five unknown samples were prepared to verify the accuracy of the prediction model. The predicted recoveries were calculated to be between 97.25% and 119.38% with an absolute t value of 0.598. The value of a t-test shows that the prediction model is accurate and reliable.
CONCLUSIONS: The present study demonstrates that the proposed method can achieve rapid detection of chlorpyrifos in wheat. © 2022 Society of Chemical Industry.
摘要：
背景：毒死蜱是农业中常用的有机磷农药。然而,其神经毒性对人类健康构成巨大威胁。在本研究中,制备了基于壳聚糖改性的基于滤纸的表面增强拉曼散射活性底物（Ch/AgNPs/paper），并用于检测120个处理过的小麦样品中的痕量毒死rif。
结果：结果表明，Ch/AgNPs/纸基材只能在浓度为0.000558mgL-1时用于增强毒死蜱的光谱指纹。拉曼光谱采集后，三种预处理方法，包括Savitzky-Golay（具有二阶多项式的Savitsky-Golay滤波器）的一阶导数和二阶导数平滑和归一化，用于减少基线变化并增加原始光谱数据集的光谱峰特征的分辨率。然后，建立了基于偏最小二乘法的小麦毒死蜱农药残留检测预测模型。归一化的偏最小二乘模型得到了最优结果，相关系数为0.9764,预测均方根误差为1.22mgL-1,相对分析偏差为4.12。制备了5个未知样本以验证预测模型的准确性。计算的预测回收率在97.25％和119.38％之间，绝对t值为0.598。t检验值表明，该预测模型是准确可靠的。
结论：本研究表明，该方法可以实现小麦中毒死蜱的快速检测。©2022化学工业学会。
结果：结果表明，Ch/AgNPs/纸基材只能在浓度为0.000558mgL-1时用于增强毒死蜱的光谱指纹。拉曼光谱采集后，三种预处理方法，包括Savitzky-Golay（具有二阶多项式的Savitsky-Golay滤波器）的一阶导数和二阶导数平滑和归一化，用于减少基线变化并增加原始光谱数据集的光谱峰特征的分辨率。然后，建立了基于偏最小二乘法的小麦毒死蜱农药残留检测预测模型。归一化的偏最小二乘模型得到了最优结果，相关系数为0.9764,预测均方根误差为1.22mgL-1,相对分析偏差为4.12。制备了5个未知样本以验证预测模型的准确性。计算的预测回收率在97.25％和119.38％之间，绝对t值为0.598。t检验值表明，该预测模型是准确可靠的。
结论：本研究表明，该方法可以实现小麦中毒死蜱的快速检测。©2022化学工业学会。
