AUTOMATED WORKFLOW FOR THE DETERMINATION OF FATTY ACID METHYL ESTERS (FAME) IN FAT AND FAT CONTAINING FOOD SAMPLES USING A 90 SEC. TRANSESTERIFICATION
Posters | | CTC AnalyticsInstrumentation
Analysis of fatty acid methyl esters is vital in regulatory, quality control and research labs for characterizing fats in foods. Traditional manual derivatization is laborious and exposes operators to hazards. Automated workflows address these challenges by enhancing safety, reliability and throughput.
This study presents a fully automated approach for FAME determination in fats and fat-containing foods using a 90 s sodium methoxide transesterification. The objectives include quantifying total fat, profiling saturated, cis- and trans-unsaturated fatty acids, and monitoring reaction completeness and side reactions through internal standards while minimizing manual handling and errors.
Samples (e.g., ~15 mg) are weighed and dissolved in dioxane containing three internal standards to track reaction steps: a non-reactive C14:1 alkane, a C11 triglyceride, and FAME-9 for saponification monitoring. An aliquot (100 µL) is transferred to a vial, reacted with 100 µL of 5 % sodium methoxide in methanol, vortexed for 10 s and allowed to react for 90 s. After adding n-heptane and vortexing, a citrate solution halts the reaction and separates phases within 60 s. The organic layer is directly injected (1 µL) into the GC. The automation software overlaps sample preparation with chromatographic runs to boost throughput.
The method achieves complete separation of FAMEs within 11 min and maintains stable peak shapes after more than 75 injections, with no detectable carry-over. Oil samples (e.g., coconut, peanut, safflower, olive, sunflower, butter) were profiled, demonstrating reliable quantitation of major fatty acids. Internal standard ratios effectively flagged incomplete transesterification or saponification, ensuring data integrity.
Further developments may include integration with online sample extraction, miniaturized reactors, alternative green reagents, coupling with mass spectrometric detection for enhanced analyte coverage and broader applications in complex matrices. Advances in software algorithms could enable real-time quality control and adaptive reaction protocols.
The automated sodium methoxide transesterification workflow provides a rapid, robust and safe method for routine FAME analysis in food fats. The use of internal standards offers rigorous control over reaction performance and ensures high data reliability with minimal manual intervention.
GC, Sample Preparation
IndustriesFood & Agriculture
ManufacturerAgilent Technologies, CTC Analytics, DataApex
Summary
Significance of the Topic
Analysis of fatty acid methyl esters is vital in regulatory, quality control and research labs for characterizing fats in foods. Traditional manual derivatization is laborious and exposes operators to hazards. Automated workflows address these challenges by enhancing safety, reliability and throughput.
Aims and Overview
This study presents a fully automated approach for FAME determination in fats and fat-containing foods using a 90 s sodium methoxide transesterification. The objectives include quantifying total fat, profiling saturated, cis- and trans-unsaturated fatty acids, and monitoring reaction completeness and side reactions through internal standards while minimizing manual handling and errors.
Methodology and Workflow
Samples (e.g., ~15 mg) are weighed and dissolved in dioxane containing three internal standards to track reaction steps: a non-reactive C14:1 alkane, a C11 triglyceride, and FAME-9 for saponification monitoring. An aliquot (100 µL) is transferred to a vial, reacted with 100 µL of 5 % sodium methoxide in methanol, vortexed for 10 s and allowed to react for 90 s. After adding n-heptane and vortexing, a citrate solution halts the reaction and separates phases within 60 s. The organic layer is directly injected (1 µL) into the GC. The automation software overlaps sample preparation with chromatographic runs to boost throughput.
Used Instrumentation
- PAL RTC workstation with robotic tool change, multi-solvent Dilutor, Vortex Mixer, Fast Wash module and 10 µL syringe
- Gas chromatograph Agilent 6890 with split injector (SSL, 250 °C, 5 mL/min split)
- BGB-WAX capillary column (25 m × 0.25 mm, 0.25 µm film)
- Oven program: 40 °C initial to 180 °C at 25 °C/min, then to 250 °C at 15 °C/min with a 3 min hold
- Flame ionization detector at 300 °C
- Data analysis with Clarity software
Main Results and Discussion
The method achieves complete separation of FAMEs within 11 min and maintains stable peak shapes after more than 75 injections, with no detectable carry-over. Oil samples (e.g., coconut, peanut, safflower, olive, sunflower, butter) were profiled, demonstrating reliable quantitation of major fatty acids. Internal standard ratios effectively flagged incomplete transesterification or saponification, ensuring data integrity.
Benefits and Practical Applications
- Improved laboratory safety by reducing manual contact with reagents
- High sample throughput: up to 50 samples in 18.5 hours via overlapping workflows
- Automated monitoring of reaction completeness and side-reactions
- Applicable to diverse food matrices, including oils, butter, cheese, salami and emulsified products
Future Trends and Possibilities of Use
Further developments may include integration with online sample extraction, miniaturized reactors, alternative green reagents, coupling with mass spectrometric detection for enhanced analyte coverage and broader applications in complex matrices. Advances in software algorithms could enable real-time quality control and adaptive reaction protocols.
Conclusion
The automated sodium methoxide transesterification workflow provides a rapid, robust and safe method for routine FAME analysis in food fats. The use of internal standards offers rigorous control over reaction performance and ensures high data reliability with minimal manual intervention.
Reference
- Arens M., Schulte E., Weber K. Fat Sci. Technol. 1994;96:67–68.
- House S.D., Larson P.A., Johnson R.R., DeVries J.W., Martin D.L. J AOAC Int. 1994;77:960–965.
- Suter B., Grob K., Pacciarelli B. Z Lebensm. Unters. Forsch. A. 1997;204:252–258.
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