结合异常样本检测的汽油族组成拉曼光谱分析

汽油族组成不仅决定了汽油品质，同时直接关系到燃烧尾气对环境的影响。文章成功地将拉曼光谱分析技术应用于汽油族组成的定量分析。通过对校正集进行异常样本检测，剔除个别异常样本，有效地提高了偏最小二乘的模型精度，得到了较好的预测效果。芳烃含量、烯烃含量和氧含量的标准预测误差分别达到了0.23，0.52和0.143，其预测复相关系数分别达到了0.987，0.927和0.971。实验结果表明：采用拉曼光谱分析技术可以有效的解决汽油族组成的定量分析问题，其分析精度显著高于近红外光谱法与多维气相色谱法;同时也适用于汽油生产过程中的在线分析。

Hydrocarbon Group Analysis of Gasoline by Raman Spectroscopy with Outlier Detection

Hydrocarbon group of gasoline not only determines the quality of gasoline, but also directly relates to the impact of exhaust air on the environment. The present paper successfully applied Raman spectroscopy to the quantitative analysis of hydro-carbon group in gasoline. Contaminated samples were removed from calibration set by outlier detection, which effectively im-proved the partial least squares (PLS) prediction accuracy. The standard prediction errors of aromatics, olefin and oxygen con-tent are 0. 23, 0. 52 and 0. 143 respectively, and the corresponding multiple correlation coefficients of prediction (R~2) are 0. 987,0. 927 and 0. 971. Experimental results show that the effectiveness of Raman spectroscopy for hydrocarbon group analysis in gas-oline, and that the prediction accuracy is much better than near infrared spectroscopy or multidimensional gas chromatography.Quantitative calibrations based on Raman spectroscopy can also be used in the on-line analysis of gasoline production processes.