Characterization and matching of oil samples using fluorescence spectroscopy and parallel factor analysis

Area of Science:

• Analytical Chemistry
• Environmental Science
• Spectroscopy

Background:

• Accurate oil sample identification is crucial for environmental monitoring and forensic analysis.
• Traditional methods like GC-FID can be time-consuming and less portable for field applications.
• Polycyclic aromatic compounds (PACs) are key indicators of oil composition and origin.

Purpose of the Study:

• To develop and validate a novel, objective method for oil sample matching using fluorescence spectroscopy.
• To establish a rapid screening technique for oil identification applicable in field or shipboard settings.
• To correlate spectral data with the composition of polycyclic aromatic compounds (PACs) for precise oil fingerprinting.

Main Methods:

• Applied Parallel Factor Analysis (PARAFAC) to fluorescence excitation-emission matrixes (EEMs) of various oil types.
• Utilized a spectral decomposition approach to identify characteristic PAC profiles.
• Validated the method by analyzing a real-world oil spill sample against a database.

Main Results:

• The PARAFAC model successfully decomposed EEMs into five factors, each linked to specific PAC groups.
• The method effectively discriminated between different oil types (heavy fuel oils, light fuel oils, lubricating oils, crude oils), with minor overlap between HFOs and crude oils.
• The Baltic Carrier oil spill sample was accurately identified as heavy fuel oil based on its PAC fingerprint.

Conclusions:

• Fluorescence spectroscopy combined with PARAFAC offers a robust and objective method for oil fingerprinting.
• This approach serves as a valuable complement to GC-FID, enabling rapid, portable oil analysis.
• The technique demonstrates high potential for environmental oil spill investigations and routine oil analysis.