利用表面增强拉曼光谱(SERS), 结合主成分分析(PCA)与分段线性回归(SLR)算法实现乙基对氧磷的定量检测。 首先采集820～1 630 cm-1乙基对氧磷溶液SERS, 并对820～1 630 cm-1(全范围)与845～875 cm-1(特征范围)光谱分别进行标准正态变换(SNV)、 多元散射校正(MSC)、 一阶导数绝对值、 二阶导数等预处理; 然后经PCA降维后利用SLR建立乙基对氧磷溶液浓度预测模型。 通过对比不同模型的预测准确度, 发现特征范围光谱采用MSC预处理后所建立的模型为最优, 总体预测均方误差值(RMSEP)为0.33, 满足乙基对氧磷定量检测的需要。

In the present paper, the surface-enhanced Raman spectroscopy (SERS) was used to build the model for the quantitative detection of ethyl paraoxon by the principal component analysis and segmented linear regression (PCA-SLR). Firstly, SERS in 820～1 630 cm-1 of ethyl paraoxon solution were measured and the spectra in 820～1 630 cm-1 (complete range) and 845～875 cm-1 (characteristic range) of ethyl paraoxon solution were preprocessed by standard normal transformation (SNV), multiplicative scatter correction (MSC), the absolute values of first derivative and the second derivative respectively. Additionally, the number of dimensions of the spectra was reduced by PCA. Finally, the models were established by SLR. It was found that the model developed with MSC preprocessed spectroscopy of characteristic range performed best (RMSEP: 0.33) by comparing the predictive accuracy of the different models. The result could meet with the needs in the quantitative detection of ethyl paraoxon.