主成分分析法和Fisher判别方法在汽油分类分析过程中的应用

Application of principal component analysis and Fisher discrimination method in the classification of gasoline

应用主成分分析(principal component analysis,PCA)法对从90#和93#两种汽油的50个实验样所取的特征数据进行降维处理,再结合Fisher判别方法对这两种汽油进行分类,并将分类结果与不采用PCA法而直接计算数据所得出的Fisher判别结果进行比较,前者的分类正确率达到100%,而后者却只有50%.结果说明采用PCA方法事先对数据处理可以大大的提高汽油分类的准确性.

Principal component analysis （PCA） and Fisher discrimination method were applied to studying the classification of 90 # and 93 # gasoline using gas chromatography-mass spectrometry （GC-MS） spectral data. It was found that when both methods were used in calculating the dataset, all the samples were classified, while when the FiSher discrimination method alone was used, only 50% of the samples were correctly classified. It shows that when PCA is used in pre-disposal of the dataset, the accuracy of classification of gasoline is improved noticeably.