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- E-ISSN 2288-8985
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Identification and classification of fresh lubricants and used engine oils by GC/MS and bayesian model
Analytical Science and Technology / Analytical Science and Technology, (P)1225-0163; (E)2288-8985
2014, v.27 no.1, pp.41-59
https://doi.org/10.5806/AST.2014.27.1.41
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(2014). Identification and classification of fresh lubricants and used engine oils by GC/MS and bayesian model. Analytical Science and Technology, 27(1), 41-59, https://doi.org/10.5806/AST.2014.27.1.41
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Abstract
The aims of this work were the identification and the classification of fresh lubricants and usedengine oils of vehicles for the application in forensic science field-80 kinds of fresh lubricants were purchasedand 86 kinds of used engine oils were sampled from 24 kinds of diesel and gasoline vehicles with differentdriving conditions. The sample of lubricants and used engine oils were analyzed by GC/MS. The Bayesianmodel technique was developed for classification or identification. Both the wavelet fitting and the principalcomponent analysis (PCA) techniques as a data dimension reduction were applied. In fresh lubricantsclassification, 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 lubricantsthan it with PCA based on dimension reduction. And we selected the Bayesian model technique with waveletfitting for classification of lubricants. The other experiment was the analysis of used engine oils which werecollected from vehicles with the several mileage up to 5,000 km after replacing engine oil. The eighty sixkinds 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 typeclassification (whether it is gasoline vehicle or diesel vehicle, only total 2 classes), the rate of matching was99.6%. In the used engine oil brands classification (total 6 classes), the rate of matching was 97.3%.
- keywords
- forensic science, lubricants, engine oil, classification, Bayesian model, wavelet fitting, GC/MS
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