快速测量十六烷值对检测柴油品质及控制炼制工艺具有重大意义。 首先对采集到的381份柴油样品进行近红外可见光谱波段全光谱扫描, 利用小波分析（WT）对原始数据进行去噪声处理, 应用竞争性自适应重加权算法（CARS）进行特征波长选择, 将CARS提取的22个特征波长输入至LS-SVM预测模型, 决定系数r2为0723, 预测均方根误差RMSEP为1878%。 结果表明, 使用WT-CARS变量选择算法获取光谱特征波长, 结合LS-SVM建模, 可以快速、 准确的测量柴油中的十六烷值, 为进一步实现柴油十六烷值的在线检测以及其他性能参数的快速测定奠定了基础。

The rapid determination of the value of cetane is important for the determination of the quality of diesel. To remove the absolute noises of the spectra, the extracted 381 absorbance spectra were preprocessed with Savitzky-Golay smoothing (SG), EMD, and Wavelet Transform (WT) methods. The preprocessed spectra were then used to select sensitive wavelengths with competitive adaptive reweighted sampling (CARS). LS-SVM were applied to build models with the selected 12 wavelength variables. The overall results showed that the LS-SVM models with the selected wavelengths based on WT preprocessed spectra obtained the best results with the determination coefficient (r2) and RMSEP were 0723 and 1878% for prediction set. The results indicated that it was feasible to use NIR with characteristic wavelengths which were obtained with CARS variable selection method, combined with LS-SVM calibration could apply for the rapid and accurate determination of diesel cetane. Moreover, this study laid a foundation for further implementation of online analysis of cetane and rapid determination of other diesel quality parameters.