摘要
研究了应用化学计量学方法解决汽油单体烃的气相色谱分析中单体烃定性库的自动选择问题。通过提取汽油单体烃谱图中的29个组分及其含量信息作为特征值,利用主成分分析法对不同工艺得到的催化裂化汽油、焦化汽油、直馏汽油、重整汽油和烷基化汽油进行分类,结合相似分析方法(即SIMCA方法)建立了各类汽油样本的类模型,借助这些类模型可以实现对未知样本的类型判别。所提出的识别方法可方便快速地判别待分析样品所属的汽油类别,并据此推荐适合该样品的定性模型库,从而实现汽油单体烃的快速、自动分析。
Chemometrics method was used to solve the problem of automatic selecting model for the detailed hydrocarbon analysis (DHA) of gasoline samples by gas chromatography/flame ionization detection (GC/FID). The 29 peaks in GC/FID DHA chromatogram and their amounts were selected as the discriminating parameters to establish the five pattern models for different gasoline samples, such as fluid catalytic cracking (FCC) gasoline, coking gasoline, straight run gasoline, reformed gasoline, and alkylation gasoline. The principle component analysis (PCA) and Soft Independent Modeling of Class Analogies (SIMCA) were used to classify the gasoline samples and to identify the unknown samples according to the above pattern models. One hundred gasoline samples, derived from known resources, were employed to validate the reliability of the sample identity technique. With the help of the pattern identity method referred here, the automation of GC/FID DHA method becomes possible.
出处
《色谱》
CAS
CSCD
北大核心
2004年第5期482-485,共4页
Chinese Journal of Chromatography
关键词
单体烃
汽油
模式识别
SIMCA
detailed hydrocarbon
gasoline
pattern recognition
Soft Independent Modeling of Class Analogies (SIMCA)
