What is FAME?

FAME stands for Fatty Acid Methyl Esters. These are compounds produced through the transesterification of triglycerides (typically from vegetable oils, animal fats, or used cooking oil) with methanol. FAMEs are the primary component of biodiesel and can enter petroleum streams intentionally (as blendstock) or unintentionally (through cross-contamination in shared infrastructure).

FAME is oxygenated, polar, and chemically distinct from conventional hydrocarbons, which creates multiple operational and compliance concerns in petroleum products—especially in jet fuel and residual fuel oil streams where FAME is not a desired component.

Why FAME Testing is Required

Jet Fuel:
Jet fuel specifications (e.g., ASTM D1655, DEF STAN 91-91) impose strict limits on FAME contamination – typically <50 mg/kg. FAME is not intended for use in turbine engines and can lead to:

  • Combustion instability
  • Fuel system deposits (coking)
  • Microbial growth due to hygroscopicity
  • Filter plugging and cold flow issues
  • Risk of flameout under certain flight conditions

FAME contamination in jet fuel often occurs during storage or transport via multiproduct pipelines that have previously carried biodiesel blends.

Fuel Oil (Residual or Distillate):
While no universal limit exists for FAME in fuel oils, testing is often necessary when:

  • FAME-containing cutter stock is used
  • Cross-contamination from storage or blending equipment is suspected
  • Fuel is being exported to markets where FAME content must be declared or controlled

Even in heavy fuel oils, FAME presence can affect stability, compatibility with other blend components, and long-term storage characteristics, especially when water uptake and microbial activity are of concern.

Diesel Fuel:
In contrast, FAME is often deliberately blended into diesel (e.g., B5, B20). In this case, testing is used to:

  • Verify blend ratio for compliance with ASTM D975, EN 590, or contractual specs
  • Confirm uniformity of blending
  • Identify nonconforming batches due to misblending or unintended contamination

Analytical Methods for FAME Determination

Several analytical methods are available depending on the fuel type and the required detection limits:

  • FTIR/MID-IR Spectroscopy
    Methods: ASTM D7371, ASTM D7861, EN 14078
    • Applicable primarily to diesel fuel
    • Detection range typically from 1.0% to 20/30% v/v FAME
    • Suitable for blend verification (e.g., B5, B20), not trace-level detection
    • Not appropriate for jet fuel testing at <50 mg/kg levels
  • Gas Chromatography (GC-FID)
    Method: EN 14103
    • Suitable for analyzing FAME feedstocks (>90%)
    • Preferred method for ensuring compliance with EN 14214
  • Gas Chromatography-Mass Spectrometry (GC-MS)
    Methods: IP 585, customized GC-MS methods
    • Detection down to 5–10 mg/kg, suitable for jet fuel and fuel oil contamination checks
    • Provides quantification and speciation of FAME components
    • Preferred method for ensuring compliance with ASTM D1655 and DEF STAN 91-91
  • Nuclear Magnetic Resonance (NMR)
    • Used for structural confirmation of FAME content in research or special investigations
    • Not typically employed for routine quality control or regulatory compliance

Considerations When Interpreting FAME Results

  • Matrix Effects: Heavier fuels may obscure low-level FAME detection without proper sample prep.
  • Sampling Technique: FAME tends to stratify or adsorb onto tank and line surfaces; representative sampling is critical.
  • Background Levels: In multi-use systems, residual FAME can persist even after multiple flushes.
  • Regulatory Requirements: Detection limits and tolerances vary by specification and jurisdiction.

Summary

FAME is no longer isolated to biodiesel—refineries must now account for it across jet fuel, diesel, and even fuel oil operations. Whether for compliance, product integrity, or operational troubleshooting, FAME testing has become a routine but essential part of quality control in the petroleum industry. Understanding which method to use, what detection limits are meaningful, and how to interpret results in context is key to ensuring reliable data for decision-making.