Advancing Exposure Assessment for Food Contaminants Through Implementation of Probabilistic Modeling
Advancing Exposure Assessment for Food Contaminants Through Implementation of Probabilistic Modeling
Risk assessments for foods, ingredients and additives have evolved over the past few decades to incorporate new techniques such as benchmark dose modeling, and threshold of toxicological concern. New developments continue to allow scientists to explore advancements in cancer risk assessment.
However, exposure assessments still tend to rely on deterministic approaches utilizing conservative inputs. Deterministic assessments are easy to conduct and easy to interpret; however, as point estimates of exposure, they do not account for variability in the level and/or likelihood of occurrence of a chemical or population differences in exposure inputs.
Probabilistic assessments can further refine deterministic assessments using distributions for various exposure inputs and, as such, better account for exposure uncertainty.
This project surveyed the current state of adoption of probabilistic exposure modeling by regulatory agencies to inform risk management decisions and regulations for food contaminants. It also developed criteria for determining high-quality distributions for input variables and highlights the value of further integration of probabilistic exposure assessments into regulatory frameworks. It concludes with a discussion of factors that hinder widespread adoption.
Greg Paoli, M.A.Sc.
Risk Sciences International
Aligned with IAFNS’ commitment to open science practices, this project has been added to the IAFNS Collection on the Open Science Framework. Note: this link redirects you to an external website.
Use of Probabilistic Exposure Models in the Assessment of Dietary Exposure to Chemicals
Food Additives & Contaminants, 2025
This paper provides an introduction and overview of the use of probabilistic exposure assessments to characterize exposures to chemicals from the diet.
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