SG平滑和IBPLS联合优化水中油分析方法的研究

快速准确地检测水中矿物油的种类与含量对污染源及时排查和控制具有重要的意义，而红外光谱分析技术检测水中矿物油具有高效、快速、无污染的优势。为获得更加可靠的分析结果应用傅里叶变换衰减全反射红外光谱(FTIR-ATR)技术获取矿物油样品的光谱信息，采用SPXY法划分样本集。对偏最小二乘法(PLS)和迭代Bagging偏最小二乘法(IBPLS)这两种建立回归模型的方法进行对比分析，还比较了采用Savitzky-Golay(SG)平滑方法与迭代Bagging偏最小二乘法(IBPLS)相结合和单一采用迭代Bagging偏最小二乘法建立回归模型的区别。通过对预测回归曲线进行对比，得出通过SG平滑的预测效果明显优于未做的。而且采用SG平滑方法和IBPLS相结合的方法建立回归模型时，汽油模型参数RMSEP为0.001 125 g·mL-1，r为0.992 5;柴油模型参数RMSEP为0.001 384 g·mL-1 ，r为0.989 3。

Research on Analysis of Oil in Water Based on the Joint Optimization of Savitzky-Golay Smoothing and IBPLS Models

Rapidly and accurately detection of the type and content of mineral oil in water pollution has important significance for the timely screening and control of pollution sources. The use of infrared spectral analysisi technology to detect mineral oil has advantanges of efficient, fast and pollution-free. Infrared spectrum technology is very for the detection of mineral oil in the water. In order to obtain a more reliable results, Fourier transforms attenuated total reflection infrared spectrometry (FITR-ATR) technology is used to get the spectral information of the mineral oil sample, and SPXY method is used to divide the sample set. The paper not only analyzed partial least squares (PLS) and iterative Bagging partial least squares (IBPLS) the two different methods to build regression model, also compared the difference of using the method of the combination of Savitzky-Golay (SG) smoothing and the method of a single iterative Bagging partial least squares (IBPLS) regression model. Based on the comparison of the predictive regression curve, we can get that the SG smooth has a better reflection on the results. And when the method of the combination of Savitzky-Golay (SG) smoothing and the method of a single iterative Bagging partial least squares (IBPLS) is used to build the regression model, the gasoline model parameters RMSEP is 0.001 125 g·mL-1, R is 0.992 5; diesel model parameters RMSEP is 0.001 384 g·mL-1, R is 0.989 3.