Mineral Oil Residues in Food: Part 5 - How to choose the right sample preparation

Importance of the Topic

Mineral oil saturated hydrocarbons (MOSH) and mineral oil aromatic hydrocarbons (MOAH) are common contaminants in food products, with potential health implications. Accurate determination of these residues is essential for food safety, regulatory compliance, and consumer protection.

Objectives and Study Overview

This work focuses on selecting and optimizing sample preparation strategies for MOSH/MOAH analysis in various food matrices using an automated liquid chromatography–gas chromatography with flame ionization detection (LC-GC-FID) system. The goal is to identify appropriate extraction, cleanup, and enrichment workflows that yield reliable, reproducible results across diverse food types.

Methodology

Sample preparation methods were adapted to the specific matrix:

• Solvent extraction (e.g., hexane) and accelerated solvent extraction for quantitative recovery of mineral oil hydrocarbons.
• Epoxidation of naturally occurring olefins (e.g., squalene, sterenes, carotenoids) to prevent interference with MOAH determination.
• Cleanup on silica gel and aluminium oxide columns to separate and enrich MOSH and MOAH fractions and to remove co-extracted fats and polar impurities.
• Control of extraction efficiency and fat loading to maintain column performance (e.g., 20 mg fat limit per LC column).

Used Instrumentation

The analytical system comprises:

• Shimadzu LC-20AD solvent delivery pumps for high-performance liquid chromatography.
• Axel Semrau Chronect LC-GC interface for seamless transfer of LC effluent to the GC.
• Shimadzu GC-2030 gas chromatograph equipped with two high-temperature columns and retention gaps for simultaneous MOSH/MOAH separation.
• Flame ionization detector for sensitive quantification of hydrocarbon fractions.

Main Results and Discussion

Tailored extraction and cleanup schemes achieved high recovery rates and effective removal of lipid and olefinic interferences. On-line LC-GC-FID provided clear separation of MOSH and MOAH fractions within a single GC run. Protocols were successfully applied to oils, fatty foods, and low-fat matrices, demonstrating method robustness and reproducibility.

Benefits and Practical Applications

• Fully automated workflow minimizes manual handling and potential contamination.
• Modular sample preparation protocols adapt to various food matrices, from vegetable oils to complex processed foods.
• High-throughput capability supports routine quality control and regulatory monitoring in food laboratories.

Future Trends and Applications

Emerging developments may include the integration of mass spectrometric detection for enhanced structural confirmation, micro-scale extraction techniques to reduce solvent consumption, and chemometric data analysis for rapid screening. Strengthening international standards and harmonized protocols will further improve interlaboratory comparability.

Conclusion

Effective MOSH/MOAH analysis in food hinges on selecting matrix-specific extraction and cleanup strategies combined with on-line LC-GC-FID separation. The proposed workflows deliver reliable quantification, ensuring compliance with safety regulations and contributing to consumer protection efforts.

References

• Biedermann M, Grob K. On-line HPLC–GC for contamination by mineral oil. Part 1: Method of analysis. J Chromatogr A. 1255:56–75 (2012).
• German Federal Institute for Risk Assessment (BfR). Measurement of mineral oil hydrocarbons in foods and packaging materials. 2018.
• Guinda A, Lanzón A, Albi T. Differences in hydrocarbons of virgin olive oils obtained from several olive varieties. J Agric Food Chem. 44(7):1723–1726 (1996).
• EN 16995:2017. Foods – Determination of MOSH and MOAH in vegetable oils and foodstuffs based on vegetable oils by on-line HPLC-GC-FID.
• Nestola M, Schmidt TC. Determination of mineral oil aromatic hydrocarbons in edible oils and fats by on-line LC–GC–FID: Evaluation of automated removal strategies for biogenic olefins. J Chromatogr A. 1505:69–76 (2017).