Detection and Quantification of Allergens in Foods and Minimum Eliciting Doses in Food-Allergic Individuals (ThRAll). (3rd December 2019)

• Record Type: Journal Article
• Authors: Mills, E N Clare; Adel-Patient, Karine; Bernard, Hervé; De Loose, Marc; Gillard, Nathalie; Huet, Anne-Catherine; Larré, Collette; Nitride, Chiara; Pilolli, Rosa; Tranquet, Olivier; Pouke, Christof Van; Monaci, Linda
• Is Part Of: Journal of AOAC International. Volume 102:Number 5(2019)
• Page Start: 1346
• Page End: 1353
• Publication Date: 2019-12-03
• Subjects: Agricultural chemistry -- Periodicals; Food -- Analysis -- Periodicals; 543
• DOI: 10.1093/jaoac/102.5.1346
• Languages: English
• ISSNs: 1060-3271

Abstract

Abstract: Risk-based approaches to managing allergens in foods are being developed by the food industry and regulatory authorities to support food-allergic consumers to avoid ingestion of their problem food, especially in relation to the traces of unintended allergens. The application of such approaches requires access to good quality data from clinical studies to support identification of levels of allergens in foods that are generally safe for most food-allergic consumers as well as analytical tools that are able to quantify allergenic food protein. The ThRAll project aims to support the application of risk-based approaches to food-allergen management in two ways. First, a harmonized quantitative MS-based prototype reference method will be developed for the detection of multiple food allergens in standardized incurred food matrices. This will be undertaken for cow's milk, hen's egg, peanut, soybean, hazelnut, and almond incurred into two highly processed food matrices, chocolate and broth powder. This activity is complemented by a second objective to support the development and curation of data on oral food challenges, which are used to define thresholds and minimum eliciting doses. This will be achieved through the development of common protocols for collection and curation of data that will be applied to allergenic foods for which there are currently data gaps.