汽油燃烧烟尘的分析与分类

Analysis and Classification of Gasoline Soot

基于裂解气相色谱-质谱联用技术(PY-GC/MS)对两种类型汽油(93#和97#)的燃烧烟尘进行了定性和定量分析.对分析结果应用主成分分析法(PCA)进行降维预处理,然后利用软独立建模分类法(SIMCA)对93#和97#两种汽油分别建立了类模型.在显著性水平α=1%的条件下,两类模型对训练集的识别率为100%,拒绝率为100%;97#模型对未知样本预测集的识别率、拒绝率均为100%;93#模型对未知样本预测集的识别率、拒绝率达到80%.研究表明PY-GC/MS结合软独立建模分类法(SIMCA)可以实现对汽油燃烧烟尘的检测分析和类型判别.

Qualitative and quantitative analyses of 93# and 97# gasoline soot were carried out based on the pyrolysis gas chromatography mass spectrometry （PY-GC/MS） technique. Principal component analysis （PCA）was applied to the reduction of dimensions of the analytical results. Two predictive models of 93# and 97# gasoline were built separately using SIMCA pattern recognition method. Under the condition of significance levelα=l%, both the identification rate and the rejection rate of the two models for the training set were 100%, while for the predictive set, the identification rate and the rejection rate of 97# model were both 100%, and those of 93# model reached 80%. Results show that PY-GC/MS with SIMCA pattern recognition method can realize the detection and classification of gasoline soot.