拉曼光谱分析方法结合表面增强技术用于亚胺硫磷农药的检测。 连续小波变换(continuous wavelet transforms, CWT)和连续投影算法(successive projections algorithm, SPA)分别用于拉曼光谱的预处理和特征拉曼位移的选择, 多元线性回归(multi-linear regression, MLR)用于特征拉曼吸收的回归分析。 结果表明, 芯片增强可以实现低浓度农药溶液拉曼响应的增强; CWT可以提高拉曼光谱的分辨率和平滑度, 消除光谱的平移误差; 基于SPA方法的特征位移选择方法, 既可以提高模型的分析精度, 又可以简化MLR模型的回归变量; 与特征增强波段的MLR模型比较, CWT-SPA-MLR模型可将验证集的预测相关系数(correlation coefficient, r)和预测均方根误差(root mean square error of prediction, RMSEP)由0.823和1.640改善为0.903和1.122。 CWT-SPA-MLR可用于拉曼光谱定量解析模型的建立, 该方法简单易用具有良好的重复性。

Raman spectroscopy combined with surface enhanced technology was adopted for analysis of phosmet pesticide. Continuous wavelet transforms (CWT) and successive projections algorithm (SPA) were used for Raman spectral preprocess and characteristic Raman shifts selection, respectively. Multi-linear regression (MLR) was used for spectral modeling. It is shown that enhanced chips can achieve enhanced Raman spectral signal for low concentration of pesticides. CWT can improve spectral resolution and smoothness, and remove translation error. Characteristic Raman shifts selection method of SPA can improve analytical precision, and simplify modeling variables of MLR. CWT-SPA-MLR model can improve correlation coefficient (r) of prediction from 0.823 to 0.903, and reduce root mean square error of prediction (RMSEP) from 1.640 to 1.122. CWT-SPA-MLR method can be used for constructing analytical models for Raman spectra and has good interpretability and repeatability.