Targeted proteomics for rapid and robust peanut allergen quantification.

Area of Science:

• Food Science
• Analytical Chemistry
• Allergen Detection

Background:

• Accurate quantification of food allergens is crucial for consumer safety.
• Existing methods for peanut allergen detection can be time-consuming and lack sensitivity.

Purpose of the Study:

• To improve the robustness, reduce analysis time, and enhance the sensitivity of LC-MS-based peanut allergen quantification in processed foods.

Main Methods:

• Optimized extraction buffer and peptide selection for method robustness.
• Developed a rapid in-solution digestion method, omitting reduction, alkylation, and clean-up steps.
• Utilized matrix-matched calibration curves for accurate quantification in food matrices.

Main Results:

• Reduced total analysis time from ~5.5-20 hours to ~2.5 hours.
• Achieved accurate quantification (CVs < 15%) with excellent linearity (0.25-1000 mg kg⁻¹) for peanut muffin and ice cream.
• Improved sensitivity with lower limits of detection (LOD = 0.25-0.5 mg kg⁻¹) and quantification (LOQ = 0.5-1.0 mg kg⁻¹).

Conclusions:

• The refined LC-MS method offers a robust and efficient approach for trace-level peanut allergen quantification.
• Methodological improvements enhance sensitivity and significantly reduce analysis time.
• The developed approach is adaptable for detecting other food-borne allergens.