Development and Validation of a QconCAT-Based LC-MS/MS Method for the Quantification of 12 Food Allergens in Foods
Yingnan Shencheng, Meizhen Zhou, Yumeng Zhao, Siyi Li, Haopeng Lin, Mohamed F. Abdallah, Rong Zhang, Shuyan Liu, Yi Li, Shupeng Yang
Abstract
Accurate detection of food allergens is crucial to ensuring food safety. This study introduces a novel LC-MS/MS method integrated with QconCAT technology for the cost-effective quantification of 12 food allergens including milk, eggs, peanuts, soybeans, wheat, sesame, and six tree nuts. Characteristic peptides were selected for specificity, stability, and resilience to heat processing using LC-HRMS, addressing inconsistencies found in prior peptide selection. Stable isotope-labeled QconCAT proteins, expressed in Escherichia coli, served as internal standards, overcoming the high cost of synthesized peptides. The proposed LC-MS/MS method, spiked with QconCAT, exhibited high sensitivity (LOD: 1–3 mg/kg, LOQ: 2–5 mg/kg), excellent linearity ( R 2 > 0.995), good accuracy (recovery: 80.2–103.5%), and precision (RSD < 13.8%). Testing of Chinese food samples revealed undeclared allergens in 33.3% of dumplings/steamed buns and 42.9% of mooncakes, demonstrating practical applicability. This cost-effective, high-throughput approach enhances multiallergen detection, supporting regulatory compliance and consumer safety.