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

Area of Science:

• Tribology and Lubrication Science
• Analytical Chemistry
• Forensic Science

Background:

• Automotive engine oils are critical for vehicle performance and longevity.
• Identifying engine oil brands and origins is important for quality control and forensic analysis.
• Elemental profiling of used engine oils offers a method for tracking and identification.

Purpose of the Study:

• To determine if elemental analysis of used engine oils can identify the oil brand, engine type, and specific vehicle.
• To evaluate the effectiveness of statistical methods in classifying used engine oil samples.
• To establish a forensic tool for tracing automotive lubricants.

Main Methods:

• Analysis of seventy-six used engine oil samples from nineteen vehicles using inductively coupled plasma-optical emission spectrometry (ICP-OES).
• Statistical comparison of elemental profiles, focusing on macro (Ca, Zn, P) and trace (wear metals) elements.
• Application of discriminant analysis for data interpretation and prediction.

Main Results:

• Elemental analysis successfully differentiated between various commercial engine oil brands.
• High prediction accuracy was achieved: 92.1% for oil brand, 82.9% for engine type, and 92.1% for individual vehicle identification.
• The study demonstrated the potential of elemental fingerprinting for forensic applications.

Conclusions:

• Elemental profiling of used engine oils is a reliable method for identifying oil brands, engine types, and individual vehicles.
• Discriminant analysis is an effective statistical tool for classifying lubricant samples based on elemental composition.
• This technique provides a valuable forensic approach for lubricant analysis and vehicle tracing.