Identification and classification of fresh lubricants and used engine oils by GC/MS and bayesian model

Abstract

The aims of this work were the identification and the classification of fresh lubricants and used engine oils of vehicles for the application in forensic science field-80 kinds of fresh lubricants were purchased and 86 kinds of used engine oils were sampled from 24 kinds of diesel and gasoline vehicles with different driving conditions. The sample of lubricants and used engine oils were analyzed by GC/MS. The Bayesian model technique was developed for classification or identification. Both the wavelet fitting and the principal component analysis (PCA) techniques as a data dimension reduction were applied. In fresh lubricants classification, the rates of matching by Bayesian model technique with wavelet fitting and PCA were 97.5% and 96.7%, respectively. The Bayesian model technique with wavelet fitting was better to classify lubricants than it with PCA based on dimension reduction. And we selected the Bayesian model technique with wavelet fitting for classification of lubricants. The other experiment was the analysis of used engine oils which were collected from vehicles with the several mileage up to 5,000 km after replacing engine oil. The eighty six kinds of used engine oil sample with the mileage were collected. In vehicle classification (total 24 classes), the rate of matching by Bayesian model with wavelet fitting was 86.4%. However, in the vehicle's fuel type classification (whether it is gasoline vehicle or diesel vehicle, only total 2 classes), the rate of matching was 99.6%. In the used engine oil brands classification (total 6 classes), the rate of matching was 97.3%.