Advancing High-Throughput MS-Based Protein Quantification: A Case Study on Quantifying 10 Major Food Allergens by LC-MS/MS Using a One-Sample Multipoint External Calibration Curve

Abstract

The LC-MS-based method has emerged as the preferred approach for quantifying food allergens. However, the preparation of a traditional calibration curve (MSCC) is labor-intensive and error-prone. Here, a sensitive and robust LC-MS/MS method for quantifying 10 major food allergens was developed and validated, where the one-sample multipoint external calibration curve (OSCC) was employed instead of MSCC. By employing the multiple isotopologue reaction monitoring (MIRM) technique with only one spiked level in the blank, OSCC can be effectively established. Results demonstrate that the proposed method exhibits excellent performance in selectivity, sensitivity, accuracy, and precision, comparable to that of the traditional MSCC. Additionally, this strategy allows for isotope sample dilution by monitoring the less abundant MIRM channel. Moreover, the developed method was successfully applied to investigate the contamination of 10 food allergens in commercial food products. With its high throughput and robustness, the MIRM-OSCC-LC-MS/MS methodology has many potential applications, especially in the MS-based protein quantification analysis.

Development of a rapid and reliable method to simultaneously detect seven food allergens in processed foods using LC-MS/MS

“…To overcome these disadvantages, various methods for analysing food allergens using high-performance liquid chromatography–tandem quadrupole mass spectrometry (LC-MS/MS) have recently been developed ( Fallahbaghery, Zou, Byrne, Howitt, & Colgrave, 2017 ; Schalk, Koehler, & Scherf, 2018 ; Croote, Braslavsky, & Quake, 2019 ; Henrottin et al, 2023 ; Lexhaller, Colgrave, & Scherf, 2019 ; Huang, Zhu, Feng, Zhang, & Zhang, 2020 ; Ma et al, 2020 ; Ramachandran, Yang, & Downs, 2020 ; Wang et al, 2021 ; Xiong, Parker, Boo, & Fielder, 2021 ; Li et al, 2022 ; Neils, Broadbent, Bose, Anderson, & Colgrave, 2022 ; Henrottin et al, 2023 ; Yang et al, 2024 ). Our research group has also previously reported on methods for simultaneous detection of wheat and buckwheat ( Seki et al, 2021 ), or walnut and almond ( Torii et al, 2023 ) in processed foods using LC-MS/MS.…”