MRM³-based LC-MS multi-method for the detection and quantification of nut allergens.

Area of Science:

• Food safety analysis
• Analytical chemistry
• Proteomics

Background:

• Food allergies pose a growing global food safety challenge.
• Existing methods for allergen detection lack sufficient sensitivity and specificity.
• Governmental regulations necessitate sensitive, quantitative methods for allergen monitoring.

Purpose of the Study:

• To enhance the analytical performance of liquid chromatography-mass spectrometry (LC-MS) for food allergen detection.
• To develop and optimize a highly sensitive method using multiple reaction monitoring cubed (MRM3) technology.
• To compare the sensitivity and applicability of MRM3 versus MRM for complex food matrices.

Main Methods:

• Employed a bottom-up proteomics approach with straightforward sample preparation.
• Developed and optimized 38 MRM3 experiments targeting 18 proteotypic peptides.
• Assessed analytical performance in three diverse food matrices, determining limits of detection.

Main Results:

• Achieved limits of detection around 1 μg/g or lower for multiple allergens (peanut, almond, cashew, hazelnut, pistachio, walnut).
• Demonstrated up to a 30-fold increase in sensitivity compared to traditional MRM methods.
• Confirmed high linearity and intersample reproducibility for quantitative analysis.

Conclusions:

• The developed MRM3-based LC-MS method represents a highly sensitive and specific tool for food allergen detection.
• This study provides the first systematic comparison of MRM3 and MRM for complex food analysis.
• The method is robust and applicable for the quantitative determination of allergenic contaminants in food products.