Classification and individualization of used engine oils using elemental composition and discriminant analysis

Abstract

The six most common commercial automotive gasoline and diesel engine oils in the Republic of Korea, ZIC A, ZIC XQ RV/SUV, Kixx G1, Kixx RV, and the brand name products HD Premium gasoline and HD Premium diesel, were randomly used in nineteen different vehicles. Samples of seventy-six used engine oils, which were withdrawn from the sumps of those vehicles at different intervals, were analyzed using inductively coupled plasma-optical emission spectrometry (ICP-OES), and statistically compared. Two data analysis strategies were used to interpret and understand the elemental profiles in the multi-dimensional data. Macro (additive elements of Ca, Zn and P) and trace (wear metal elements of Ag, Al, Ba, Cd, Cr, Cu, Fe, Mg, Mo, Na, Ni, Pb and Sn) elements were used as potential markers to determine the brand of oil used and the engine type in which the oil was used, and to trace the individual vehicle for forensic purposes. The discriminant analysis statistical technique was applied, and its prediction ability was assessed. In this study, 92.1%, 82.9% and 92.1% of the cross-validated grouped cases correctly predicted the brand of oil, the engine type and the vehicle that was the source of the oil, respectively.