Targeted proteomics for rapid and robust peanut allergen quantification

Abstract

This study improves LC-MS-based trace level peanut allergen quantification in processed food by refining method robustness, total analysis time and method sensitivity. Extraction buffer (six compared) and peptide choice were optimised and found to profoundly affect method robustness. A rapid extraction and in-solution digestion method was developed omitting subsequent reduction, alkylation and sample clean-up steps effectively reducing total analysis time from the previously reported ∼5.5–20 h to ∼2.5 h. For the three best performing peptides, accurate quantification (CVs < 15%) with matrix-matched calibration curves (R2 = 0.99–0.97) was achieved for peanut muffin and ice-cream with excellent linearity (0.25–1000 mg kg−1). The best performing peptide enabled excellent recovery rates in ice-cream (106.0 ± 15.1%) and peanut muffin (72.7 ± 13.4%). Sensitivity (LOD = 0.25–0.5 mg kg−1; LOQ = 0.5–1.0 mg kg−1) was 2- to 20-fold improved compared to previous methods depending on the peptide. These methodological improvements contribute to robust peanut detection in food and can be translated to additional food-borne allergens.