Multivariate pattern recognition of petroleum-based accelerants by solid-phase microextraction gas chromatography...

Area of Science:

• Analytical Chemistry
• Forensic Science
• Chemometrics

Background:

• Accurate identification of petroleum-based fuels is crucial in forensic investigations.
• Traditional methods for accelerant analysis can be complex and time-consuming.
• Standardized guidelines, such as those from ASTM International, are essential for consistent classification.

Purpose of the Study:

• To develop and evaluate a novel method for the extraction, analysis, and identification of petroleum-based fuels.
• To apply multivariate data analysis techniques for improved classification accuracy.
• To assess the effectiveness of Principal Component Analysis (PCA) and Soft Independent Modeling by Class Analogy (SIMCA) for accelerant grouping.

Main Methods:

• Solid-phase microextraction (SPME) for sample extraction.
• Gas chromatography with flame ionization detection (GC-FID) for fuel analysis.
• Multivariate data analysis, including PCA and SIMCA, for data simplification and classification.

Main Results:

• SIMCA models achieved high accuracy in predicting unknown sample classes: 98.5% for the previous ASTM system and 97.2% for the current system.
• The combined approach of SPME and multivariate data analysis proved effective for accelerant classification.
• PCA and SIMCA demonstrated utility in establishing accelerant groupings based on established guidelines.

Conclusions:

• SPME coupled with multivariate data analysis offers a novel and efficient approach to accelerant sampling and classification.
• This method provides a robust framework for the accurate identification of petroleum-based fuels in forensic contexts.
• The developed models show significant potential for routine use in forensic laboratories.