摘要
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(1^(st)D和2^(nd)D)、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R_p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(1 st D and 2 nd 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.
作者
刘燕德
叶灵玉
唐天义
欧阳爱国
孙旭东
张宇
LIU Yan-de;YE Ling-yu;TANG Tian-yi;OUYANG Ai-guo;SUN Xu-dong;ZHANG Yu(School of Mechatronics Engineering,East China Jiaotong University,Nanchang 330013,China)
出处
《光谱学与光谱分析》
SCIE
EI
CAS
CSCD
北大核心
2018年第9期2741-2748,共8页
Spectroscopy and Spectral Analysis
基金
国家自然科学基金项目(61640417)
国家"十二五"(863计划)课题(SS2012AA101306)
江西省优势科技创新团队建设计划项目(20153BCB24002)
南方山地果园智能化管理技术与装备协同创新中心(赣教高字[2014]60号)
江西省研究生创新资金项目(YC2015-S238)资助