The initial steps in estimating dietary exposure to contaminants include gathering the necessary expertise, clarifying the intent and purpose of the work, selecting a dietary exposure model, and gathering available pertinent information. Expertise is generally needed in chemistry, agriculture, toxicology, statistics, nutritional epidemiology, and computer software development. The goal might be to determine the average exposure of a population to contaminants, to identify demographic groups within a population that are especially vulnerable to a contaminant, to evaluate the regulation of agricultural and food-manufacturing practices, or to determine compliance with standards for local and/or imported foods. Examples of dietary exposure models include the core food model, directed core food model, large database model, raw agricultural commodity (RAC) model, regional diet model, duplicate diet model, and total diet composite model. Each model has advantages and disadvantages and different costs and resource requirements. Consideration of the sources and flow of selected contaminants though the food supply may help identify the best exposure model to use. Pertinent information that may already be available includes analytical data on contaminants in foods or commodities, government regulations pertaining to the levels of contaminants in foods, food-consumption data, data on the average body weights of age-gender groups (to express exposure on a body weight basis), and biochemical measures of contaminants or their residues/metabolites. Collecting available information helps to clearly define what critical information is missing so that the planned research can be most effective. Careful documentation of decisions and assumptions allows for recalculating exposure estimates with the same model using different decisions and assumptions; documentation also allows others to understand what was done and how to use the resulting intake estimates properly. Clearly identifying the limitations of the exposure model may provide justification for additional resources to further refine and improve the model.

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