利用中红外光谱和化学计量学实现了对乙醇柴油各项性能指标的定量分析。 实验样品96个, 为32种不同浓度的乙醇柴油溶液。 采用S-G平滑、 MSC、 微分处理(1stD和2ndD)、 SNV等四种方法对光谱数据进行预处理, 并结合八种波段筛选方法(UVE, CARS, SPA, RPLS, UVE-SPA, UVE-CARS, SPA-CARS, UVE-SPA-CARS)对乙醇柴油MIR光谱数据进行处理, 分别建立乙醇柴油密度、 粘度、 乙醇含量的PLSR模型, 得出以下主要结论: 综合比较八种变量筛选方法, 发现UVE-SPA-CARS-PLS对乙醇含量的建模效果最好, 模型预测集的Rp和RMSEP分别为0.978 1和0.825 5。 变量筛选较原始光谱建立的模型来说, 不仅模型输入数量减少, 预测效果也有所提高。

The quantitative analysis of the performance indexes of ethanol diesel was carried out by means of medium infrared spectroscopy and chemometrics. There were 96 samples in 32 different concentrations of ethanol diesel oil solutions. Using S-G, MSC, smooth differential processing (1st D and 2nd D), SNV of methods preprocessed spectral data, combined with the screening method of eight kinds (UVE, CARS,SPA,RPLS,UVE-SPA,UVE-CARS,SPA-CARS,UVE-SPA-CARS)processing ethanol diesel MIR spectral data, and PLSR model were established respectively with density of ethanol diesel oil, viscosity and ethanol content. The results showed that: comparing the eight variables screening methods, we found that UVE-SPA-CARS-PLS has the best modeling effect on ethanol content. The correlation coefficient was 0.978 1 and the root mean square error of prediction was 0.825 5, respectively. Compared with the model established by the original spectrum, variable selection not only reduced the number of model inputs, but also improved the prediction effect.