High-resolution mass spectrometry-based selection of peanut peptide biomarkers considering food processing and market type variation. (30th January 2020)

• Record Type: Journal Article
• Authors: Gavage, Maxime; Van Vlierberghe, Kaatje; Van Poucke, Christof; De Loose, Marc; Gevaert, Kris; Dieu, Marc; Renard, Patsy; Arnould, Thierry; Gillard, Nathalie
• Is Part Of: Food chemistry. Volume 304(2020)
• Publication Date: 2020-01-30
• Subjects: High-resolution mass spectrometry -- Peptide biomarker selection -- Processed food products -- Multiple allergen isoforms -- Peanut origin; Food -- Analysis -- Periodicals; Food -- Composition -- Periodicals; 664
• DOI: 10.1016/j.foodchem.2019.125428
• Languages: English
• ISSNs: 0308-8146

Abstract

Highlights: Strategy for peptide biomarker selection for allergen detection and quantification. Empirical identification of 16 potential peptide biomarkers for peanut with HRMS. Analysis of processed peanut matrices produced in semi-industrial conditions. Protein variation due to peanut market type. Consideration of multiple allergen isoforms. Abstract: To protect allergic patients and guarantee correct food labeling, robust, specific and sensitive detection methods are urgently needed. Mass spectrometry (MS)-based methods could overcome the limitations of current detection techniques. The first step in the development of an MS-based method is the identification of biomarkers, which are, in the case of food allergens, peptides. Here, we implemented a strategy to identify the most salient peptide biomarkers in peanuts. Processed peanut matrices were prepared and analyzed using an untargeted approach via high-resolution MS. More than 300 identified peptides were further filtered using selection criteria to strengthen the analytical performance of a future, routine quantitative method. The resulting 16 peptides are robust to food processing, specific to peanuts, and satisfy sequence-based criteria. The aspect of multiple protein isoforms is also considered in the selection tree, an aspect that is essential for a quantitative method's robustness but seldom, if ever, considered.