Rapid identification of adulteration in edible oilsusing direct analysis mass detection platform (RADIAN-ASAP-LiveID)

Significance of the Topic

Sesame oil commands a high market value and is frequently adulterated with lower-cost vegetable oils, undermining quality control, consumer safety, and brand trust. Rapid authentication methods are crucial to ensure product integrity and regulatory compliance.

Objectives and Study Overview

The aim was to establish a swift, user-friendly protocol for detecting adulteration in edible oils using the RADIAN ASAP-LiveID direct analysis platform. The study targeted pure sesame, corn, and soybean oils, as well as sesame oil blended with 20% corn or soybean oil.

Instrumentation

• RADIAN ASAP system (compact single quadrupole MS with Atmospheric Solids Analysis Probe)
• MassLynx software V4.2 for data acquisition
• LiveID software V2.0 for chemometric modeling and real-time identification

Methodology

Oil samples were prepared by diluting 30 µL of oil in 970 µL n-hexane and applying 2 µL to the ASAP probe. Key ionization settings included a corona current of 3 µA, desolvation temperature of 600 °C, N₂ flow 3 L/min, and cone voltage 15 V. Full-scan mass spectra (m/z 50–1200) were collected at 2 Hz. Multivariate analysis employed PCA-LDA (5 PCA and 3 LDA components) for classification.

Main Results and Discussion

• Distinct mass spectral fingerprints enabled clear separation of pure sesame, corn, and soybean oils, and their adulterated mixtures.
• PCA-LDA model exhibited 96.63% cross-validation accuracy with no misclassifications.
• Real-time identification generated results within seconds, with total analysis time under two minutes per sample.

Benefits and Practical Applications

• Minimal sample preparation and parameter optimization.
• Rapid, near-instantaneous analysis ideal for field and in-line screening.
• Accessible workflow requiring no specialized LC-MS expertise.
• Enhanced throughput supports quality control in food production and regulatory inspections.

Future Trends and Opportunities

• Expansion of chemometric libraries to cover a broader range of food matrices.
• Integration with artificial intelligence for automated anomaly detection.
• Development of portable or handheld AIMS devices for on-site testing.
• Combining AIMS with complementary techniques (e.g., NMR, spectroscopy) for multi-modal authentication.

Conclusion

The RADIAN ASAP-LiveID platform demonstrates a powerful, efficient approach for edible oil authentication. By coupling direct ambient ionization with chemometric analysis, it delivers rapid, reliable identification of pure and adulterated oils, reinforcing quality assurance in the food industry.

Reference

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