An integrated calibration strategy for the development and validation of an LC-MS/MS method for accurate quantification of egg allergens (Gal d 1-6) in foods.

Allergen quantification Calibration curves Food allergen LC-MS Validation methods

Journal

Food chemistry
ISSN: 1873-7072
Titre abrégé: Food Chem
Pays: England
ID NLM: 7702639

Informations de publication

Date de publication:
04 Nov 2023
Historique:
received: 10 07 2023
revised: 08 10 2023
accepted: 31 10 2023
medline: 19 11 2023
pubmed: 19 11 2023
entrez: 18 11 2023
Statut: aheadofprint

Résumé

Accurate determination of egg allergens in food is vital for allergen management and labeling. However, quantifying egg allergens with mass spectrometry poses challenges and lacks validation methods. Here, we developed and validated an LC-MS/MS method for quantifying egg allergens (Gal d 1-6) in foods. Sample extraction, enzymatic digestion, purification, proteins/peptides selection, and calibration curves were optimized. VMVLC[+57]NR (Gal d 1) and GTDVQAWIR (Gal d 5) exhibited outstanding sensitivity and stability, serving as quantitation markers for egg white and yolk. Using a matrix-matched calibration curve with allergen ingredients as calibrants and labeled peptides as standards, we achieved highly accurate quantitation. Validation involved spiking egg protein into egg-free foods, showing excellent sensitivity (LOQ: 1-5 mg/kg), accuracy (62.4 %-88.5 %), and reproducibility (intra-/inter-day precision: 3.5 %-14.2 %/8.2 %-14.6 %). Additionally, we successfully applied this method to commercial food analysis. These findings demonstrate optimal allergen selection, peptides, and calibration strategy are crucial parameters for food allergen quantification via MS-based methods.

Identifiants

pubmed: 37979263
pii: S0308-8146(23)02540-2
doi: 10.1016/j.foodchem.2023.137922
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

137922

Informations de copyright

Copyright © 2023 Elsevier Ltd. All rights reserved.

Déclaration de conflit d'intérêts

Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Auteurs

Shupeng Yang (S)

Institute of Food Science and Technology, Chinese Academy of Agricultural Sciences, Beijing 100193, People's Republic of China. Electronic address: yangshupeng@caas.cn.

Jingjing Chen (J)

Institute of Food Science and Technology, Chinese Academy of Agricultural Sciences, Beijing 100193, People's Republic of China.

Mohamed F Abdallah (MF)

Depaerment of Food Technology, Safety and Health, Ghent University, Coupure Links 653, 9000, Ghent, Belgium; Department of Forensic Medicine and Toxicology, Faculty of Veterinary Medicine, Assiut University, Assiut 71515, Egypt.

Haopeng Lin (H)

Institute of Food Science and Technology, Chinese Academy of Agricultural Sciences, Beijing 100193, People's Republic of China.

Peijie Yang (P)

Institute of Food Science and Technology, Chinese Academy of Agricultural Sciences, Beijing 100193, People's Republic of China.

Jianxun Li (J)

Institute of Food Science and Technology, Chinese Academy of Agricultural Sciences, Beijing 100193, People's Republic of China.

Rong Zhang (R)

Institute of Food Science and Technology, Chinese Academy of Agricultural Sciences, Beijing 100193, People's Republic of China.

Qianqian Li (Q)

Institute of Food Science and Technology, Chinese Academy of Agricultural Sciences, Beijing 100193, People's Republic of China.

Peng Lu (P)

Institute of Food Science and Technology, Chinese Academy of Agricultural Sciences, Beijing 100193, People's Republic of China.

Shuyan Liu (S)

Institute of Food Science and Technology, Chinese Academy of Agricultural Sciences, Beijing 100193, People's Republic of China. Electronic address: liushuyan@caas.cn.

Yi Li (Y)

Institute of Food Science and Technology, Chinese Academy of Agricultural Sciences, Beijing 100193, People's Republic of China. Electronic address: liyi01@caas.cn.

Classifications MeSH