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ARS Home » Northeast Area » Beltsville, Maryland (BHNRC) » Beltsville Human Nutrition Research Center » Nutrient Data Laboratory » Research » Research Project #426560

Research Project: USDA National Nutrient Databank for Food Composition

Location: Nutrient Data Laboratory

Project Number: 8040-52000-064-00-D
Project Type: In-House Appropriated

Start Date: Feb 20, 2014
End Date: Feb 19, 2019

The mission of the Nutrient Data Laboratory is "To develop authoritative food composition databases and state of the art methods to acquire, evaluate, compile, and disseminate composition data on foods and dietary supplements available in the United States." The following three objectives and nine sub-objectives provide the infrastructure for completing planned research over the next five years and the guiding principles for accomplishing the research with a clear, scientific focus. Objective 1. Develop and expand the USDA-ARS food composition databases to represent the dynamics of the U.S. food supply, including increased use of commercially packaged foods, restaurant foods, school foods, and ethnic foods. Sub-Objective 1.A. Update the USDA National Nutrient Database for Standard Reference (SR) to represent the dynamics of the current U.S. food supply, including increased availability and variety of commercially packaged, restaurant, school, and ethnic foods. Sub-Objective 1.B. Expand and update existing food yield and nutrient retention factor tables to reflect current food preparation methods and food products. Sub-Objective 1.C. Provide nutrient composition data for use in the national survey, What We Eat In America (WWEIA), NHANES. Sub-Objective 1.D. Monitor sodium and related nutrients in commercially processed and restaurant foods in the U.S. food supply. Objective 2. Develop authoritative food composition databases for non-nutritive components that may promote health; examples include isothiocyanates and other sulfur-containing compounds. Expand existing databases, including flavonoids, to include more foods, variability estimates, and other information (cultivar, weather, growing conditions, etc.), which impact the nutrient values. Sub-Objective 2.A. Expand and update accurate representative values for a number of bioactive compounds in raw, processed, and prepared foods in different Special Interest Databases (SID). Sub-Objective 2.B. Develop a new Special Interest Database (SID) on the content of sulfur-containing bioactive compounds in selected foods, with special emphasis on variability and factors, e.g., cultivar, location, and growing conditions, which potentially could influence variability. Sub-Objective 2.C. Determine the effect of various preparation methods on the content of various bioactive compounds in selected foods. Objective 3. Identify, evaluate, and develop new methods for the acquisition, evaluation, compilation, and dissemination of food composition data from diverse sources through modernization of existing and development of new, robust information technology. Sub-Objective 3.A. Provide easy-to-use, web-based mechanisms for data submission. Sub-Objective 3.B. Enhance dissemination routines in the National Nutrient Databank System (NDBS) via automated methods to expand the types of data formats available on NDL’s web site.

Multiple methods for obtaining data will be used (e.g., nationwide product sampling and analysis, collaborations [food manufacturers, agricultural scientists], and publicly available information). The National Food and Nutrient Analysis Program (NFNAP) generates high-quality, analytical data for U.S. foods and includes a rigorous scientific process to develop nationally representative estimates of means and variability, under USDA analytical oversight. The 5 aims are: a) identify/prioritize foods/nutrients for analysis; b) devise/implement nationally based sampling plan(s); c) analyze food samples; and d) review, compile, and disseminate data. Beef data will be evaluated by nutrient and cut and compared across primals (chuck, brisket, etc.) and cooking methods. New meat data processed through the National Data Bank System (NDBS) will support calculations of cooking yield and retention factors (means, variances, and associated 90% or 95% confidence intervals). NDL will identify SR foods to be added or updated; this list will be provided to FSRG to prioritize foods needed in WWEIA (e.g., commercially processed and restaurant foods to replace home recipes), ensuring WWEIA adequately represents respondent reports. For important new foods, NFNAP sampling may include analytical data available for the next survey-SR dataset; standardized NDBS imputation procedures will be used to calculate missing values. NDL analyzed each Sentinel Food (SF; 2010-2013) for all nutrients; they will be reanalyzed every 4-8 years depending on budget and priority using the Principal Axis Factoring (Factor Analysis): consumption frequency in WWEIA, NHANES, 2009-10; potential for reduction (New York City’s National Salt Reduction Initiative targets); and history of change in the market. NDL will obtain and disseminate data for sulfur-containing bioactive compounds, detailing other factors (cultivars, location, weather, growing conditions) which impact concentration and focusing on genus Brassica, Allium; samples will be obtained through NFNAP and analyzed by ARS-Food Composition and Methods Development Laboratory (FCMDL) and sources and magnitudes of variability studied. Food selected for analysis for non-nutritive components will be based on flavonoid content, lack of analytical data, and potential for developing retention factors for related foods. The proanthcyanidin database will be expanded using formulations (linear regression techniques) developed for SR, to provide values to FSRG for foods reported in WWEIA-NHANES. Standardized, user-friendly databases will be released; collaboration with ILSI North America/Agricultural Technology Innovation Partnership (ATIP) Foundation and the food industry will be explored to expand the number of brand name foods. ATIP will develop/manage a new portal to facilitate submission of food manufacturers’ brand name nutrient data, strengthening NDL database for policy makers, researchers and the public. In parallel, NDL will work with other partners to identify infrastructure improvements to the NDBS.