Assessment of the intake of proteins requires data on the composition of foods as they are consumed as well as on the amounts that are consumed. Unfortunately, the collection of the appropriate data to determine either the composition of the foods or the amounts that are consumed is much more complicated than this would imply. This chapter discusses the types of data, the methods that are available to collect each type of data, and finally the methods for combining the data to produce estimates of intake. The final step of the process is to assess the meaning of the estimated intakes, e.g., to compare the estimated intakes with relevant nutritional reference values to assess the adequacy of the intakes. The results can also be used to confirm that intakes are not excessive. The analyst must have the intended application in mind in designing the intake assessment in order to select the most appropriate data and models. Typically, the process will be conducted for the general population, as well as critical groups that are expected to be have significantly different intakes than the general population, e.g., infants, children, ethnic subgroups.
The objective of the dietary intake assessment must be clearly identified before the appropriate input data may be selected. For example, will the results of the evaluation be used to determine whether consumers have adequate protein intakes, or will it be used to determine whether too much of a protein is being consumed? Will it be used to evaluate the potential for allergic reactions or for other types of endpoints? Is the frequency of intake of the protein of relevance? How do the levels of the protein to be evaluated compare to the total protein in the diet?
A framework for conducting the assessment should be established that will allow the analyst to select the most appropriate methodology for the intended use of the assessment. A framework that includes a stepwise approach is recommended. In general, the framework's early steps will include screening methods that use minimal resources and the shortest possible time, and will use reasonable but conservative assumptions, e.g., which will tend to underestimate essential nutrients and possibly overestimate other substances.
The methodology applied should be clearly stated and reproducible. Information about the model and data sources used, assumptions, limitations, and uncertainties should be documented. The assumptions concerning concentration levels and consumption patterns upon which dietary intake estimates are based need to be fully described.
Uncertainties in food component concentration data can be reduced by improving the quality of the data available. Data quality is defined to include the suitability of the sampling plan in order to obtain representative samples of food; appropriateness of sample handling procedures; selection and validation of the analytical methodology; use of analytical quality control programs; and the number of samples, determined based on statistical characteristics of each data set. Early identification of the foods contributing most to the estimated intakes can assist in directing resources to the most important foods.
The criteria that will be applied to establish that the data are appropriate for the intended application need to be clearly defined and provided to users of the data. This information should be sufficiently complete to make critical decisions concerning the appropriateness of decisions based on the available data and analysis methods.
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