Evaluation and Improvement of Nutrient Composition Data for Trace

Chapter 8

Evaluation and Improvement of Nutrient Composition Data for Trace Elements in Foods

Downloaded by NORTH CAROLINA STATE UNIV on August 2, 2012 | http://pubs.acs.org Publication Date: December 26, 1991 | doi: 10.1021/bk-1991-0445.ch008

Wayne R. Wolf Nutrient Composition Laboratory, U.S. Department of Agriculture, Beltsville, MD 20705

Uses for trace element nutrient composition data in foods for dietetic, nutrition, and epidemiological purposes are greatly increasing. There is a large amount of presently available composition data and there is a strong demand for generating new data. Ability to assess the quality of reports and information stemming from these uses of the available composition data rests directly upon a good knowledge of their reliability. An accurate assessment of this reliability is needed. Due to the complexity and scope of the presently available data, this assessment requires a multifaceted evaluation process utilizing computerized technology. For the first step of this assessment, multidisciplinary criteria have been formulated, and an expert software system developed for evaluating the available published data on selenium content in foods. In order to assess reliability of calculations using different nutrient composition databases, a comparison of six USDA databases has been carried out. This comparison looked at data and calculations from these databases using a common list of foods representative of U.S. dietary intake. The discussion in this paper will focus mainly on the general area of nutrient composition data, of which trace elements are an important part and serve as specific examples. Nutrient composition data in foods are used for a wide variety of dietetic, nutritional, and epidemiological purposes. These composition data are typically made use of in composition databases to estimate dietary nutrient intake via calculations based on food consumed. These calculated estimations of intake are used to formulate menus designed to meet some defined requirements for nutrient intakes, usually in institutional or medical situations. Calculated intakes are also used in nutrition research stud les to define individual nutrient requirements. For example, studies are carried out to define the metabolism of the trace elements; including understanding the role and interactions trace elements have on the metabolism and utilization of other nutrients, both at the macro and trace levels; and to determine the bioavailability of the trace elements. This chapter not subject to U.S. copyright Published 1991 American Chemical Society In Biological Trace Element Research; Subramanian, K., et al.; ACS Symposium Series; American Chemical Society: Washington, DC, 1991.

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BIOLOGICAL TRACE ELEMENT RESEARCH

Although composition databases are sometimes used to calculate dietary exposure or intake of individuals, it is well recognized that the variability and uncertainty in these estimates can be very significant. Many of the variabilities and uncertainties in the calculated estimates are inherent in the data and in the use of composition databases.


Sampling and Representative Data

Composition data on the nutrient content of a specific food are not single static numbers, because the samples representing that food constantly change. In addition to analytical variation in generating the data, foods have natural variability from sample to sample depending upon a number of factors. These factors include origin (geographic changes in soil or climate), processing, distribution and genetic differences in variety or type. For example, in a recent study reporting the selenium content in bread, coefficients of variation among samples were approximately 50%(1). Complete composition information on each and every variety of food sample is not available. Composition data in databases are a compilation of available information, usually merging data from several sources. The values are representative of a food or food type and not a specific sample. The data can only be as representative as the underlying sampling plans which generated the analyzed samples. Often the information available in the databases is insufficient to ascertain the representativeness of data in relation to the specific use. The data may represent every possibility from single samples to extensive sampling plans representative of the nation's food supply. Composition data in many databases are representative of fewer individual samples rather than many. The "science" (in reality an art) of sampling the food supply for purposes of generating nutrient composition data is practiced only in a very few locations which are also sources of analytical data. An important example of a nationwide sampling plan is the US Food and Drug Administration's Total Diet Study Program (2). This nationwide monitoring program, which does include determination of a number of trace elements, will be discussed in more detail later in this paper. In order to have a truly representative nationwide sampling scheme, information on factors such as, marketing and product distribution must be statistically combined with the ability to procure, transport, and most importantly composite and subsample the collected food materials. The important role of representative sampling within a Q A scheme for determining trace constituents in food has been summarized by Horwitz, et.al.(3). In addition to variabilities in the composition databases themselves, estimations of the exact types and amounts of food consumed by individuals are a complex and difficult task(4). Thus, although concern with individual menu or meal planning is becoming more important, and more use of databases and dietary calculations are appearing, considerable caution should be reserved in interpreting information based upon calculated estimates of nutrient intake of individuals. More often databases are used to calculate dietary intakes and exposures for population groups in order to gain information on public health concerns. Population

In Biological Trace Element Research; Subramanian, K., et al.; ACS Symposium Series; American Chemical Society: Washington, DC, 1991.

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surveys and population intakes are often combined with other population information on health statistics in the context of epidemiological studies. For example, dietary intake calculations are used to formulate dietary recommendations in terms of nutrient intake for health or toxic concerns for trace elements. There are no nutritional requirements for specific foods, only for the nutrients supplied in foods. What people eat is defined in terms of kinds and amounts of foods. To get from food intake to nutrients and nutrient requirements requires translation from food to nutrients. This is done either infrequently by analyzing directly the individual foods consumed or most often, by calculation using nutrient composition databases. Thus the body of information used in making any decisions, statements or public policy about health or disease concerns relating to nutrition rests almost solely on a foundation of nutrient composition databases. The reliability of this information and of the resultant decisions or policy, are dependent directly upon the soundness of these databases.

Evaluation And Improving Quality Of Analytical Data

A whole body of metrological knowledge and published information describes ways to evaluate and improve individual analytical data(5-7). This knowledgewill not be further discussed in detail here but includes areas such as: improved and correct analytical methodology; quality control in practice of methodology; and validation and quality assurance of methodology through use of food reference materials. As stated above, the quality of information used in public policy decisions relating to nutrition rests very strongly on the foundation of information generated by use of nutrient composition databases. These databases are composed of combined individual values usually from a number of sources. Sometimes databases contain imputed or estimated values in the absence of available analytical data. Ability to validate the information generated by these databases, rests directly upon our ability to assess the quality of the individual data contained in the databases. As an approach to development of methodology for evaluating available composition data, criteria have been established to assess and quantitatively rate published data on selenium in foods(&). These criteria were developed in five categories: number of samples; analytical method; sample handling; sampling plan; and analytical quality control. Ratings based upon these criteria were assigned to data from each individual published study. A Quality Index (QI) derived from the ratings over all categories was assigned to each reported value. Criteria were defined for an acceptable QI and data with less than the acceptable QI were omitted from further consideration. The QFs for acceptable data were then summed over each food to determine a Confidence Code(CC) associated with the mean selenium content of that food. This CC was intended to indicate the relative degree of confidence the user can have in each selenium value. A confidence code of "a" means the user can have con-


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siderable confidence in the value. A confidence code of "c" means the user can have less confidence due to limited quantity and /or quality of the data. A computerized expert system has been developed(£) using these criteria and a Table of selenium values ranking the important food sources of selenium has been publishedQO). The criteria have been adapted to evaluation of copper content of foods and a Table of evaluated data published(H). The concepts and approach used in this evaluation have potential for extension to many other sets of nutrient composition data in addition to the potential for establishment of criteria for evaluation of proposed analytical methods.

Assessment Of Nutrient Composition databases

Evaluation of the confidence in the individual data contained in databases is the first step in establishing the reliability of use of these databases. Several additional questions must be asked in using calculations from a number of databases: * How compatible are the various calculated results? * How valid are these calculated results? In order to gain some information about these questions, a study has been carried out to compare nutrient intake calculations generated from six US Department of Agriculture nutrient composition databases(12). The Nutrient Data Research Branch of the Human Nutrition Information Service(HNIS) in Hyattsville, M D , maintains and provides information on the nutrient composition of foods important to the American diet. HNIS maintains a computerized database which is utilized primarily for the Continuing Survey of Food Intake by Individuals(CSFID(T 3). Release 4 of this database was used in this comparison. In addition each of the five Human Nutrition Research Centers (located in Grand Forks, N D ; Boston, M S ; Beltsville, M D ; Houston, TX; and San Francisco, CA) maintains and utilizes a computerized nutrient composition database for calculations of dietary intakes for their research studies and mission oriented responsibilities. Each of these groups by virtue of location and mission would have interest in different nutrient data needs. Although each of the groups is using an independent database, due to interactions in historical development there are strong ties among several. The HNIS, Grand Forks and Beltsville databases were developed originally with the Houston, Boston and San Francisco databases each developing respectively from one of the original. A l l of the databases are based on U S D A standard reference tapes, with data additions and changes in user software developing in each center. For scientific validity in comparing nutritional findings among these U S D A centers, comparability of calculated results among their nutrient databases is required. Several specific goals of the study relating to evaluation of the databases were: estimate completeness of data in each database; identify and categorize nutrient data differences; and identify multiple data sources (12).



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For this evaluation, each Center provided a calculated nutrient content of a representative daily diet based upon an identical list of 200 foods. This list of fociis was obtained from the Food and Drug Administrations Total Diet Study (TDS) programQ). In the TDS program, 200 individual foods and food items, representative of diets of the adult population, are collected in three cities within four regions of the United States. Four to five collections per year are obtained. The individual foods from each collection are sent to the FDA Kansas City Laboratory where they are prepared ready to eat, composited, and each of the 200 food item composites analyzed for a series of constituents for a nationwide monitoring program. Determination of trace element content of these foods is one part of the FDA monitoring program(2). In addition a complete nutrient profile is being generated on composite samples of these foods as a supplement to a program being carried out in conjunction to the normal FDA-TDS Study. (Tanner, J.T.; Iyengar, G.V.; Wolf, W.R.; Fresenius J. Anal. Chem., In Press, 1990). Thus a large amount of validated analytical information will be available for this list of foods. This list of foods was developed by FDA using extensive nationwide dietary survey data and is fully representative of foods mostfrequentlyconsumed in the US diets. For the USDA database evaluation, each center was asked to code these 200 foods and supply a data tape with the individual food composition information and calculation of total nutrient in a simulated composite based upon a given amount of each individual food. These tapes were forwarded to the Boston HNRC computer center and comparisons were carried out on 28 nutrients common to all six databases. As a first step for comparison, mean values, standard deviations and coefficients of variation(CV) were determined for the total intake across the six centers. Results of these comparisons have been reported( 12). One immediately evident result was that not all databases had complete information on all 28 of the nutrients compared. For those nutrients where there was complete information in all databases, the CV's were in general lower than 10%. For example CV's for calories, protein, carbohydrate, total fat, cholesterol,riboflavin,niacin, vitamin C, iron, calcium, potassium and phosphorus were all lower than 5%. For the additional trace elements compared, zinc and copper had CV's over 20% and magnesium had 17%, all three showing missing data in several databases. While the complete comparison of values for each of the trace elements in the individual foods is too lengthy for summation in this paper, there were significant differences seen in the data among the six centers. These differences were seen to be predominately due to incomplete data in several of the databases. A full evaluation of these results is underway. In summary, for accurate estimation of individual nutrient intakes the actual foods consumed MUST be analyzed. For populations, intake calculations from composition databases may be adequate, providing the data have been properly evaluated and validated.

Literature Cited 1. Holden, J. M.; Gebhardt, S., Davis, C.S., and Lurie, D.C. A Nationwide Study of the Selenium Levels and Variability in Bread Products, 74th Annual Meeting, Fed. of Amer. Soc. Exper. Bio., Washington D.C., April 1990, (Abstract).


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2. Pennington, J.A.T. Revision of Total Diet Study Food List and Diets, J. Am. Diet. Assoc; 1983,82,166-173. 3. Horwitz, W.; Kamps, L. R.; Boyer, K.W. Quality Assurance in the Analysis of Foods for Trace Constituents, J. Assoc. Off. Anal. Chem. 1980, 63,1344-1354.


4. Human Nutrition Information Service, USDA: Food Intakes: Individuals in 48 States, Year 1977-78. Nationwide Food Consumption Survey 1977-78, Report No.I1, 1983. 5. Wolf, W. R.; Ihnat, M. Evaluation of Available Certified Biological Reference Materials for Inorganic Nutrient Analysis, In Biological Reference Materials, Wolf, W.R., Ed.,John Wiley & Sons, New York, 1985,pp 89-105. 6. Uriano, G.Α.;Cali, J. P. The Role of Reference Materials and Reference Methods in the Measurement Process, In Validation of the Measurement Process DeVoe, J. R., Ed., American Chemical Society 1977, Chapter 4. 7. Wolf WR, Quality Assurance for Trace Element Analysis, In Trace Elements in Human and Animal Nutrition. 5th Ed., Mertz, W., Ed., Vol 1, Acad. Press, 1987 pp 57-78. 8. Holden, J. M.; Schubert, A. S.; Wolf, W.R.; Beecher, G.R. A System for Evaluating the Quality of Published Nutrient Composition Data: Selenium , A test Case. In Food Composition Data: A UsersPerspective.,Rand, W.,;Windham, C. T.; Wyse, B.; Young, V.; Eds., Food and Nutrition Bulletin. Suppl. 12, 177-193, 1987 9. Bigwood, D.W.; Heller, S.R.; Wolf, W.R.; Schubert, A.S.; Holden, J.M. SELEX, An 'Expert System For Evaluating Published Data on Selenium in Foods Anal. Chem. Acta., 1987,200, 411-419. 10. Schubert, A.S.; Holden, J.M.; Wolf, W.R. Selenium Content of a Core Group of Foods Based on a Critical Evaluation of Published Analytical Data, J. Of Am. Diet. Assoc 1987,87,285-289. 11. Lurie, D.G.; Holden, J.M.; Schubert, A.S.; Wolf W. R.; Miller-Ihli, N.J. The Copper Content of Foods Based on a Critical Evaluation of Published Analytical Data, J. Food Composition and Analysis: 1989, 2, 298-316. 12. Scura, L.; Wolf, W.R.; Rand, W. Comparison of Six Nutrient Databases Within the U.S. Department of Agriculture, Proc. 13th Nutrient Databank Conference: Framingham, Mass. May 1988, ppl25-142. 13. U.S. Department of Agriculture, Human Nutrition Information Service, 1986. USDA Nutrient database for Individual Intake Surveys, Rel. 2, Springfield Va: National Tech. Information Service. Acc. No. PB86-206299/HBF. Computer Tape. RECEIVED August 14, 1990


Evaluation and Improvement of Nutrient Composition Data for Trace

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