Modelling allergenic risk

Sophie Birot

Research output: Book/ReportPh.D. thesis

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Abstract

Up to 20 million Europeans suffer from food allergies. Due to the lack of knowledge about why food allergies developed or how to protect allergic consumers from the offending food, food allergy management is mainly based on food allergens avoidance. The iFAAM project (Integrated approaches to Food Allergen and Allergy Management) aims at developing strategies for food allergies based on evidences.

Especially, food allergen risk assessment helps food producers or authorities to make decisions on withdrawing a food product from the market or adding more information on the label when allergen presence is unintended. The risk assessment method has three different kinds of input. The exposure is calculated from the product consumption and the allergen contamination in the food product. The exposure is then compared to the thresholds to which allergic individuals react in order to calculate the chance of allergic reaction in the population.

In allergen risk assessment, the emphasis was on the threshold data, and no effort was made on consumption data. Moreover, no pan-European consumption data suitable for allergen risk assessment are available. A procedure for grouping food products automatically across countries is proposed. Thus, the allergen risk assessment can be performed cross-nationally and for the correct food group.

Then the two probabilistic risk assessment methods usually used were reviewed and compared. First order Monte-Carlo simulations are used in one method [14], whereas the other one combines second order Monte-Carlo simulations with Bayesian inferences [13]. An alternative method using second order Monte-Carlo simulations was proposed to take into account the uncertainty from the inputs. The uncertainty propagation from the inputs to the risk of allergic reaction was also evaluated for all the methods using uncertainty analysis [11].

The recommended approach for the allergen risk assessment was implemented in a Shiny application with the R software. Thus, allergen risk assessment can be performed easily by non-statisticians with the interactive application.
Original languageEnglish
Place of PublicationKgs. Lyngby
PublisherTechnical University of Denmark
Number of pages204
Publication statusPublished - 2017
SeriesDTU Compute PHD-2016
Number441
ISSN0909-3192

Cite this

Birot, S. (2017). Modelling allergenic risk. Kgs. Lyngby: Technical University of Denmark. DTU Compute PHD-2016, No. 441
Birot, Sophie. / Modelling allergenic risk. Kgs. Lyngby : Technical University of Denmark, 2017. 204 p. (DTU Compute PHD-2016; No. 441).
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abstract = "Up to 20 million Europeans suffer from food allergies. Due to the lack of knowledge about why food allergies developed or how to protect allergic consumers from the offending food, food allergy management is mainly based on food allergens avoidance. The iFAAM project (Integrated approaches to Food Allergen and Allergy Management) aims at developing strategies for food allergies based on evidences.Especially, food allergen risk assessment helps food producers or authorities to make decisions on withdrawing a food product from the market or adding more information on the label when allergen presence is unintended. The risk assessment method has three different kinds of input. The exposure is calculated from the product consumption and the allergen contamination in the food product. The exposure is then compared to the thresholds to which allergic individuals react in order to calculate the chance of allergic reaction in the population. In allergen risk assessment, the emphasis was on the threshold data, and no effort was made on consumption data. Moreover, no pan-European consumption data suitable for allergen risk assessment are available. A procedure for grouping food products automatically across countries is proposed. Thus, the allergen risk assessment can be performed cross-nationally and for the correct food group. Then the two probabilistic risk assessment methods usually used were reviewed and compared. First order Monte-Carlo simulations are used in one method [14], whereas the other one combines second order Monte-Carlo simulations with Bayesian inferences [13]. An alternative method using second order Monte-Carlo simulations was proposed to take into account the uncertainty from the inputs. The uncertainty propagation from the inputs to the risk of allergic reaction was also evaluated for all the methods using uncertainty analysis [11]. The recommended approach for the allergen risk assessment was implemented in a Shiny application with the R software. Thus, allergen risk assessment can be performed easily by non-statisticians with the interactive application.",
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Birot, S 2017, Modelling allergenic risk. DTU Compute PHD-2016, no. 441, Technical University of Denmark, Kgs. Lyngby.

Modelling allergenic risk. / Birot, Sophie.

Kgs. Lyngby : Technical University of Denmark, 2017. 204 p. (DTU Compute PHD-2016; No. 441).

Research output: Book/ReportPh.D. thesis

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Birot S. Modelling allergenic risk. Kgs. Lyngby: Technical University of Denmark, 2017. 204 p. (DTU Compute PHD-2016; No. 441).