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Nutrient data bases—Considerations for educators
CONTINUING EDUCATION ARTICLE
Nutrient Data 8asesConsiderations for Educators Loretta W. Hoover1 and Suzanne Pelican 2
JDepartment
of Human Nutrition, Foods, and Food Systems Management, University of Missouri-Columbia, Columbia, Missouri 65211 2Nutrition Program, The Pennsylvania State University, University Park, Pennsylvania 16802
The purpose of this article is to examine the sources and limitations of nutrient data and data bases, and to discuss some educational issues surrounding their selection and use in nutrient analysis programs. LIMITATIONS OF NUTRIENT DATA Properly used, nutrient data can provide valuable information, but educators must be aware of data limitations: potential sources of error, confusion, and/or variability related to the data's dynamic and inexact nature.
State of methodologies. A primary data limitation involves nutrient assay techniques; researchers are still developing and refining laboratory methods for determining many nutrients in foods. Table 1 shows the current state of nutrient methodologies as rated by the USDA Nutrient Composition Laboratory. Nutrition educators might anticipate that methodologies for assaying nutrients such as fiber, trans-fatty acids, and heme iron are rated as "conflicting" or "lacking." However, educators might be surprised that the methodologies for determining food energy, vitamin A, and vitamin C are also rated as "conflicting." To use nutrient data intelligently, educators need to know the state of the various methodologies for determining nutrients. Without such knowledge educators are illprepared to understand and unable to explain to target audiences why changes in data occur as methodologies improve. For example, we would expect to see modification in vitamin C data for some foods with changes in the current methodology. Isoascorbate, a stereoisomer of ascorbic acid, is being used increasingly by the food industry in place of BHA and BHT. Although isoascorbate possesses little, if any, vitamin C biological value, current vitamin C assay procedures do not differentiate between the two compounds. Because isoascorbate in foods can produce false high vitamin C values, 58
JOURNAL OF NUTRITION EDUCATION
USDA is currently working to increase specificity of the vitamin C assay (1, Note 1).
Growing conditions and storage variation. A second limitation is the variability in nutrient content of food items resulting from differences in growing conditions, stage of ripeness, and handling procedures (2, 3). As nutrition professionals, our long-standing awareness of some of these sources of variation may prompt us to gloss over them quickly, but in doing so, we may lose sight of their potential magnitude. Watt points out, for example, that if a potato is stored for six months after harvest, there is a threefold decline in vitamin C content (3). Partly in response to these forms of variability, the Department of Agriculture, in its data base compilation, averages values from several reliable sources for many food items. These averagings are representative or standard reference values reflecting the approximate nutrient content of foods on a year-round, nationwide basis (2, Note 2). As such, these values are useful in the development of educational materials on food composition, in menu planning, in inventory analysis, in dietary analysis of populations, etc.; but use of representative values is not appropriate in situations such as metabolic studies, which require exact values for specific food items. Food preparation. Changes in nutrient content as a result of food preparation can be a third limitation. Educationally, this limitation is important because of the growing commercial promotion of nutrient analysis software for recipe analysis. To accurately estimate a recipe's nutrient content, a program should account for nutrient losses. Although accounting for nutrient losses involves computer programming, we feel the issue of recipe analysis warrants consideration as a data base limitation for two reasons. First, nutrient retention information needed to write such programs is, arguably,
nutrient data, and second, some nutrient analysis software data bases provide compiled values for recipe items. Educators and other software users may need, therefore, to scrutinize nutrient data bases and accompanying programs in terms of the technique used to compute nutrient values from recipes. Although there are several ways to estimate nutrient content of a recipe, Merrill et af. (4, Note 3) report the method based on yield and nutrient retention factors, used to estimate five nutrients in selected foods in the 1963 edition of Agriculture Handbook No.8 (5). USDA has also released a provisional table (6, Note 3) with percent retention values for fifteen vitamins and minerals in approximately eighteen cooked foods (e.g., sweet potatoes, beet) and food groups (i.e., legumes, fruits). Regardless ofthe calculation method used, Agriculture Handbook No. 102 (7, Note 3) is a comprehensive compilation of the yields of foods in various stages of preparation and is useful for estimating changes in weight for ingredients. Educators who use nutrient data bases for recipe analysis should recognize that nutrient retention data is far from complete and that it is difficult, with or without a computer program, to estimate nutrient values from a recipe. Nevertheless, educators can make some gross qualitative distinctions using basic knowledge of heat lability and water solubility of nutrients. For instance, given a calculated nutrient analysis of a cranberry recipe, values for energy and total fat are likely to be more accurate than the values for thiamin and vitamin C because energy and total fat are less affected by heat and water than are thiamin and vitamin C. Rogan and Yu (Journal ofNutrition Education, 16:65-66, 1984) offer some additional examples of variations in nutrient content as a result of food preparation.
Cbanging food supply. A fourth limitation involves data changes resulting from alteraVOLUME 16
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Table 1
State of development of methods for nutrients in foodb
Nutrient Category
Table 2
State of Methodologya Conflicting
Adequate Substantial
Carbohydrates, fiber, and sugars
Individual sugars
Lacking
Fiber Starch
Energy
Food energy
Lipids
Cholesterol Sterols Trans-fatty acids Fat (total) Fatty acids (common)
Printed Publication Name and Number (Most Recent Publication Date)
Computerized Data Set TapeU and Release Number (Release Date)
Home and Garden Bulletin No. 72 (1981)b
72-1 release 1 (l977)C release 2 (l982)b
Handbook No. 456 (1975)b
456-3 release 1 (l977)C release 2 (l981)C release 3 (1982)C release 4 (1983)b
Handbook No.8 (1%3)b
8-1-0 (l968)C,d 8-1-1 (l972)C Nutrient Data Base for Standard Reference release 1 (1981)C release 2 (l982)C release 3 (l983)b
Minerals/Inorganic Calcium Iron (total) Copper nutrients Selenium Magnesium Phosphorus Potassium Sodium Zinc
Arsenic Chromium Fluorine Iodine Manganese
Proteins and amino Nitrogen acids (total)
Amino acids (most)
Amino acids (some) Protein (total)
Vitamins
Niacin Riboflavin Thiamin Vitamin lUi
Vitamin A Carotenes Vitamin B-12 Vitamin C VitaminD VitaminE Folacin Pantothenic acid
Biotin Choline VitaminK
Handbook No.8 Revised sections 8-/ (1976)b 8-2 (l977)b 8-3 (l978)b 8-4 (1979)b 8-5 (l979)b 8-6 (1980}b 8-7 (1980)b 8-8 (1982)b 8-9 (1982)b 8-/0 (l983)b
Substantial
Conflicting
Lacking
Good
Fair Slow High
aSee Note 4 bCurrently available cNo longer available dReleased on cards, not magnetic tape
Slow
Method modification
Method development! modification Extraction procedures Applications
aDescriplion of methodology states: Adequate Factors Accuracy
Speed of analysis Cost per analysis
Development need5
Excellent Fast Modest
(<$100)
Moderate Modest to high
Extraction procedures Applications
Cobalt Heme-iron Molybdenum Nonheme-iron Silicon Tin Vanadium
Selected USDA nutrient data bases
Poor
Method development Extraction procedures Applications
binformation released by !he Nutrient Composition Laboratory, Agricultural Research Service, U.S. Department of Agriculture, 1983. Table taken from: Beecher, G. R. and J. T. Vanderslice. Determination of nutrients in foods. In Modern methods of food analysis, K. K. Stewart and J. R. Whitaker, ed. Westport, CT: AVI Publishing. (In press)
tions in enrichment standards, the introduction of new foods into the marketplace, and the development of new preparation and processing techniques. These shifts underscore the almost inevitable change in nutrient data, independent of improvements in methodology. For example, the releases of computerized data set 72-1 in 1977 and 1982 (Table 2) reflect different enrichment standards (Note 2). Thus, the release number, which corresponds to the release date, of a USDA data set indicates its currentness and, therefore, its accuracy. One way that the introduction of new foods into the marketplace can contribute to data limitations involves the addition of data for brand name products to a data base. Fast-food data, for example, are common brand name additions to a data base. Although insertion of such data can add specificity and educational utility to a data base, such additions may also introduce VOLUME 16 NUMBER 2
1984
data "gaps" or incomplete data because values may not be available for all of the nutrients included for the original items in the data base. For example, if a data base manager adds a new food item with values for thirteen nutrients but the data base contains values for twenty nutrients for its other food items, then the new food item will have data gaps for seven nutrients. Furthermore, the quality of added data can vary greatly. For instance, some data base developers/managers use USRDA percentages taken from food labels to calculate nutrient values for insertion into a data base. However, such calculations are not appropriate values for most data bases. In conjunction with the question of including data by brand name, the USDA position may interest some data base users: USDA does not usually report nutrient content of foods by brand name because, in most cases, the variability within one brand
is as great as the variability among different brands. Therefore, for those products, values represent the food more accurately when data are averaged across brands (generically) rather than within brands. USDA has, however, reported breakfast cereals by brand name in one of the revised sections of Handbook No. 8 (8) because foods such as breakfast cereals cannot be described generically (9). SOURCES OF DATA BASES USDA. In 1960 and 1977 other researchers (2,10) comprehensively summarized the history and development of many data bases, including those of USDA. Since then, USDA has continued development of its National Nutrient Data Bank (NDB) operated by the Department's Consumer Nutrition Division (CND). The NDB is a computer-based management system that stores nutrient data, but it also includes programs to edit, query, and average individual nutrient values for any given food item (9). The term "USDA data" does not necessarily mean that a USDA laboratory generated the values in question; it means that the reliability of the data has been evaluated by USDA. Although USDA conducts extensive chemical analyses, other sources, such JOURNAL OF NUTRITION EDUCATION
59
as the food industry, university researchers, and other Government laboratories provide much data that are evaluated on the basis of sampling techniques, methodology, replication procedures, etc. This screening of data is a key step in generating representative values that are incorporated into USDA data bases. The NDB is a source of nutrient data in both published and machine-readable or computerized forms (9). Table 2 lists some commonly used USDA nutrient data bases (both printed and computerized) that are either currently available or that have been available and are, therefore, presently in use with many nutrient analysis programs. Several aspects of Table 2 warrant clarification or emphasis. In both Home and Garden Bulletin No. 72 (11) and Agriculture Handbook No. 456 (12) considerable data variations exist between the published and computerized versions. At least two factors contribute to this difference: a) the computerized tapes contain more current data as indicated by the more recent release dates and b) the computerized tapes contain imputed or estimated nutrient values in places where blank spaces appear in the publication (9, Note 2). The variability between printed and computerized versions raises another reference problem: Saying that a given nutrient analysis program uses "USDA data" does not specify the data base. Even identifying a data base as being "No. 72," for example, is inadequate because it is unclear whether the developer transcribed printed values from Home and Garden Bulletin No. 72 to a computerized medium (e.g., floppy disk) or whether the developer purchased a computerized tape from USDA and either used the values directly or transferred the data to another computerized medium. Furthermore, even if the source (i.e., printed form or computerized tape) is apparent, users need to know the ''vintage,'' i.e., the currentness of the data. Thus, when the data base is on computerized tape, developers should indicate the tape's release number and date. A developer using Home and Garden Bulletin No. 72 should also indicate the publication date because it has been revised several times. Table 2 also illustrates the confusion that results from specifying a data base as being "Handbook 8," a phrase we often hear. The publication date of the last complete revision of printed Handbook No.8 (5) is 1963. Although the Handbook is comparatively old, all of the newer data bases contain much of the same data that appears in this 60
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1963 edition. However, USDA has undertaken a massive revision to expand Handbook No.8 to include over 4000 foods and to provide data for eleven additional nutrients. The revision includes values for nine minerals, nine vitamins, cholesterol, and amino acids and fatty acids for most foods; data on dietary fiber are also provided for some foods. The revision of Handbook No. 8 is being released in sections; each section reports values of a particular food group. To date, USDA has released ten printed sections (8); the eleventh section, on vegetables, is in press (9). The completed revision of Handbook No. 8 in printed form will consist of over twenty food group sections. The computerized versions of the revision are contained in the Nutrient Data Base for Standard Reference. As new sections of the Handbook are completed, USDA updates this Standard Reference data base and releases a new version identified by a release number and date (Table 2). Until the revision of all twenty food groups is complete, releases of the Standard Reference will be supplemented with older data so that purchasers receive a complete data base with as many current values as are available. As with the other computerized data sets, USDA inserts imputed values for most of the blank spaces in the data base (9). These values are "flagged" on the tape, by means of a code, to alert a user that the data are not based on analytical values (9). Prior to release of revised sections of Handbook No.8, USDA does make some new data available to the public through publication of provisional tables of nutrient composition. These new data may be limited to a given nutrient such as zinc (13), or may cover several nutrients for a group of foods such as beverages. Provisional values are subject to change prior to release of the data in revised sections of Handbook No.8. Other nutrient data bases. In the past two decades, researchers, commercial software firms, and other individuals have compiled expanded nutrient data bases to include foods and nutrients not available in USDA publications (2). These expanded data bases include both print (14) and computerized forms; the current edition of the Nutrient Data Bank Directory (15, Note 5) lists 55 computerized data bank systems, although many other systems exist. Systems that are listed in this directory tend to be extensive data collections with values for many nutrients and different types of food and dietary supplements, and they have widespread applicability ranging from clinical and research
purposes to educational uses. Although we may generally consider a nutrient data base to be a large collection of values, we should not overlook the fact that some educational programs with a specific focus use a small data base. For example, at least one microcomputer program (16) for lay audiences provides analyses only of fast-food items and, therefore, uses a very limited data base. Cautions. In some sense, every nutrient analysis program has its "own" nutrient data base. Realistically, however, most nutrient data bases have evolved from data compiled by USDA (2) with values from other sources added to this USDA "core" (Note 2). This raises the need to emphasize an obvious but frequently overlooked point: Although data from an original source may be sound, manipulation of data (e.g., transferral from original source to computerized medium; routine data base management procedures, etc.) may introduce errors not present in the original source. There is, therefore, no substitute for competent nutrient data base developers and managers. Hoover and Perloff (17, Note 5) have developed several computer tasks to diagnose computational errors in a computer program designed to perform nutritional intake analyses. These exercises can assist in detecting incorrect data although computational errors can also involve improper procedures. Some nutritionists (18) support development of an accreditation process for nutrient analysis software that could help safeguard users against faulty data bases as well as poor programming. DATA BASE SELECTION Tradeoffs. In selecting a nutrient data base, nutrition educators face numerous tradeoffs that vary with instructional goals, the needs of target audiences, and the resource limitations ofthe educational setting. These tradeoffs include data base size or comprehensiveness, completeness, currentness, and sources. Hardware and software associated with a data base are also major considerations, but a full discussion of these as tradeoff factors is beyond the scope of this paper. In terms of data base size and comprehensiveness, a large data base with a wide selection of food items can have advantages. If many specific food items are available, a user is less likely to require food item substitutions when entering dietary intake information. Because substitutions can introduce inaccuracies, a large data base can VOLUME 16
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reduce this source of error. However, as the size of a data base increases, the chances of introducing errors in compiling and managing it increase. Also, if lay audiences use a program directly, they may become frustrated trying to locate items in a large data base. Regarding the issue of size, data base users should note that the number of food item entries in a data base may differ from the number of uniquely described food items because some data bases store the same food item with varying portion sizes. This arrangement can be advantageous educationally if it assists a target audience in specifying portion size. However, the arrangement does not increase the comprehensiveness of the data base, and it increases both data base maintenance responsibilities and the chances for introducing errors. For example, if a single food item appears in a data base three separate times with different serving sizes, the data base manager must make changes in three entries rather than one when updated information is available for the food item. In addition to the quantitative aspects of data base size or comprehensiveness, there is a qualitative aspect regarding the types of foods and nutrients included in a data base. Educators should approach this qualitative issue on the basis of the needs of their settings. For example, a data base that provides values for commercial baby foods might be useful in teaching a nutrition class to new parents. By contrast, such data is of little value to an instructor who uses a nutrient analysis program as part of a weight control class for adults. Educational settings may also require inclusion of specific nutrients. For instance, a nutritionist who supervises a consumer education booth at a shopping mall might feel that sodium and total fat data are essential for effective outreach. The issue of completeness overlaps with data base size and comprehensiveness. As we have indicated, expanding a nutrient data base can create gaps in the data. How these gaps are handled is important to any data base user, including educators. Left unchanged or unmarked, gaps can create analysis errors if the program enters a zero where, in reality, there is a measurable nutrient amount. Therefore, some data base developers and managers choose to flag missing data and/or to impute values. Depending on the type and extent of incomplete data, few educators would want a data base that does not account for data gaps, but there may be tradeoffs between flagging and imputing data. For example, VOLUME16 NUMBER2
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an output that flags incomplete data could be confusing to learners. On the other hand, although imputing values may make an output easier to interpret, the values may not be reasonable estimates unless they have been made by a person with substantial knowledge about nutrient data. Although we have repeatedly highlighted the importance of currentness, there are also tradeoffs with this factor. Current values should be accurate, but constant updating also increases the likelihood that erroneous data will enter the system. Continuous updating is also very costly. Furthermore, some educational settings may not require the most recent values. If the primary purpose of an analysis program were, for example, to increase a lay audience's awareness of commonly eaten foods, the program's data base may not need to utilize the most up-to-date values. On the other hand, faculty supervising a graduate program in clinical dietetics may decide that there is a justifiable need to maintain the most current data base. There are also tradeoffs with data base sources. We have stressed the reliability of USDA data, but USDA cannot meet the data needs of all users and all situations. For instance, USDA does not provide data by brand name for fast foods, yet educators often see a need for this information and turn to other sources. Similarly, until the revision of Handbook No. 8 is finished, USDA will not have complete data for the eleven additional nutrients that are being included for the first time. Thus, some data base developers/ managers piece together, from a variety of sources and/or by imputing values, sets of data for nutrients not yet available from USDA for many foods. Although we stress the importance of these added data being reliable, firm judgment of reliability is a virtually impossible task for a potential data base user. In general, however, food companies; published articles; and commercial, government, and university laboratories can provide valuable and usable data. With any source of nutrient data, users should consider limitations such as the state of methodologies for analysis.
Evaluation questions. Perloff (Note 2) developed a series of questions to assist potential data base users in appraising commercial nutrient data bases. As a summary to our discussion, we adapted Perloffs series to produce a set of questions that focuses on the needs of nutrition educators. We stress that the list is a tool to assist in the examination of a data base. It is not complete
for any particular educational setting. Additionally, we refer readers to the discussion by Murphy, King, and Calloway (Journal of Nutrition Education, 16:73-75, 1984) on selecting a nutrient data base and analysis program for use with college students. Our questions to assist with initial evaluation of a data base are as follows: A. Data base core - What data base constitutes the core? - If the core is not from USDA, does the source appear to be reliable? - What is the core's date of release or publication? - Is the release or publication date optimal for your needs? B. Supplementary data - What data, if any, have been added to the core data base? - Do the sources for the supplementary data appear to be reliable? - Do supplementary data include values for nutrients that have methodologies rated as "lacking?" - Are there precautions taken to ensure reliability and accuracy of the supplemental data, and if so, are these precautions adequate for your needs? C. Total data base - Is the data base size (quantitative and qualitative comprehensiveness) optimal for your needs? - Is the data base complete for your needs? - If the data are incomplete, are gaps accounted for in a way that is acceptable, given your needs? D. Data base management - Are all personnel involved with development and management of the data base knowledgeable and competent about nutrient data and data base management? - Is information about the data base core, past updates, and indusion of supplementary data well-documented, readily available, and understandable? D ACKNOWLEDGMENTS
We gratefully acknowledge Betty Perloff for her valuable assistance in preparing this paper. NOTES
I
Personal communication, G. R. Beecher, Chief, USDA Nutrient Composition Laboratory, Building 161, Beltsville Agricultural Center East, Beltsville, MD 20705, February 2, 1984. 2 Perloff, B. Nutrient data bases: Availability, options and reliability. In Proceedings of the JOURNAL OF NUTRITION EDUCATION
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Eighth National Nutrient Data Bank Conference, R. Tobelmann, ed. Springfield, VA:
U.S. Department of Commerce, National Technical Information Service. (In press) 3 Available from: Nutrient Data Research Branch, Human Nutrition Information Service, U.S. Department of Agriculture, Hyattsville, MD 20782. 4 Available from: National Technical Information Service, U.S. Department of Commerce, 5285 Port Royal Road, Springfield, VA 22161. 5 Available from: Department of Human Nutrition, Foods and Food Systems Management, College of Home Economics, 217 Gwynn Hall, University of MissouriColumbia, Columbia, MO 65211. LITERATURE CITED
1
2
document ARS 62-13, by A. L. Merrill, C. F. Adams, and L. J. Fincher. Washington, DC: 5
6
7
8
National Academy of Sciences. National Research Council. Food and Nutrition Board. Recommended dietary allowances, 9th ed. Washington, DC: National Academy of Sciences, 1980. Hertzler, A. A., and L. W. Hoover. Review of nutrient data bases: Development of food tables and use with computer. Journal of the
American Dietetic Association 70:20-31, 3 4
1977. Watt, B. K. Tables of food composition: Uses and limitations. Contemporary Nutrition 5(2):1-2, 1980. U.S. Department of Agriculture. Procedures
9
for calculating nutritive values of homepreparedfoods. Agriculture Research Service
U.S. Department of Agriculture, 1966, 35 pp. U.S. Department of Agriculture. Composi-
tion of foods-Raw, processed, prepared. Agriculture handbook no. 8, by B. K. Watt and A. L. Merrill. Washington, DC: Government Printing Office, 1963, 190 pp. U.S. Department of Agriculture. Consumer Nutrition Division. Nutrient Data Research Group. Provisional table on percent retention of nutrients in food preparation. Hyattsville, MD, October 1982, 2 pp. U.S. Department of Agriculture. Food yields
summarized by different stages of preparation. Agriculture handbook no. 102, by R. H. Matthews and J. G. Young. Washington, DC: Government Printing Office, 1975. U.S. Department of Agriculture. Composi-
Agriculture, 1984, pp. 292-99. 10 Todhunter, E. N. Food composition tables in the U.S.A. Journal of the American Dietetic Association 37:209-14, 1960. 11 U.S. Department of Agriculture. Nutritive
value offoods. Home and garden bulletin no. 72, Washington, DC: Government Printing
Office, 1981, 34 pp. 12 U.S. Department of Agriculture. Nutritive
value of American foods in common units. Agriculture handbook no. 456, by C. F.
13
14
tion of foods-Raw, processed, prepared. Agriculture handbook no. 8-1: Dairy and egg products, 1976, 144 pp.; 8-2: Spices and herbs, 1977,43 pp.; 8-3: Baby foods, 1978, 231 pp.; 8-4: Fats and oils, 1979,142 pp.; 8-5: Poultry products, 1979, 330 pp.; 8-6: Soups, sauces, gravies, 1980,228 pp.; 8-7: Sausages and luncheon meats, 1980, 92 pp.; 8-8: Breakfast cereals, 1981, 160 pp.; 8-9: Fruits andfruitjuices, 1982,283 pp.; and 8-10: Pork products, 1983, 206 pp., Washington, DC:
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Government Printing Office. Rizek, R. L., F. N. Hepburn, and B. P. Perloff. USDA's nutrient data bases. In Proceedings of the 1984 USDA Agricultural Outlook Conference, October 31-November 3, 1983. Washington, DC: U.S. Department of
Columbia Printing Services, 1981, 76 pp. 18 Sorenson, A. W., R. Seltser, and B. Wyse. Personal computers for health. The Professional Nutritionist 15(1):1-3,6, 1983.
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Self-Assessment Question nai re After reading the Continuing Education Article, "Nutrient data bases-Considerations for educators," please answer the following questions by indicating your responses on the Self-Assessment Questionnaire Answer Form located on the next page. Registered dietitians should return the completed form, with a check for $10.00 to cover processing, to The Society for Nutrition Education, 1736 Franklin Street, Oakland, CA 94612. This activity has been approved for one hour of Continuing Education Credit by the Commission on Dietetic Registration. Answers to the selfassessment questionnaire can be found on page 76. Questionnaires must be returned within six months of their appearance in the Journal in order to be eligible for credit. Notification will not be sent if hour is approved.
MULTIPLE CHOICE: For items 1 to 18, select the ONE best answer. 1. Which of the following is NOT true? A. Conflicting methods exist for assaying fiber and trans-fatty acids. 62
JOURNAL OF NUTRITION EDUCATION
Adequate assay methods have been developed for both vitamin A and C. C. The assay methods for vitamin K and biotin are lacking. D. both A and C
2.
Which of the following variables have prompted USDA to average values for many food items? A. growing conditions of crops B. stage of crop ripeness C. food handling procedures D. all of the above
3.
Representative values generated by USDA reflect the approximate nutrient content of A. foods available year-round across the nation. B. similar types of foods. C. a standard or key food in a food group. D. commonly eaten foods. The representative values calculated by USDA are NOT useful in A. dietary analysis of populations.
review of nutrient data base system capabilities. Columbia, MO: University of Missouri-
B.
educational materials on food composition. C. metabolic studies. D. inventory analysis.
B.
4.
Adams. Washington, DC: Government Printing Office, 1975, 291 pp. Murphy, E. W., B. W. Willis, and B. K. Watt. Provisional tables on the zinc content of foods. Journal of the American Dietetic Association 66:345-55, 1975. Pennington, A. T., and H. N. Church. Food values of portions commonly used. New York, NY: Harper and Row Publishers, 1980, 186 pp. Eighth National Nutrient Data Bank Conference. Nutrient data bank directory, by L. W. Hoover, T. Fisher, and D. Hay. 3rd edition. Columbia, MO: University of MissouriColumbia Printing Services, December 1983, 21 pp. Schrank, J. Fast food micro-guide. The Learning Seed, Kildeer, IL, 1983. Hoover, L. W., and B. P. Perloff. Modelfor
5.
Which of the following is NOT true concerning name brand data? A. Food labels provide data that is sufficiently exact for most data bases. B. Brand name data can add specificity and educational utility to a data base. C. Brand name data may not be available for all of the nutrients included for other foods in a data base. D. both Band C
6.
Which of the following is TRUE concerning USDA and nutrient data for name brand foods? A. USDA does not report any nutrient values for name brand foods. B. USDA reports name brand nutrient values for breakfast cereals. C. USDA generally averages nutrient values across brands of the same food. D. both B and C VOLUME16
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7.
Which of the following reasons explains the variations in nutrient data between the published and computerized forms of Handbook No. 456? A. The computerized tapes contain more current nutrient data. B. The computerized tapes contain some estimated nutrient values when exact values are not known. C. Nutrient information on a greater number of foods can be stored on the computerized form. D. both A and B
8.
Agriculture Handbook No. 102 con-
SELF-ASSESSMENT QUESTIONNAIRE ANSWER FORM Expiration Date: December 1984 Continuing Education Article "Nutrient data bases - Considerations for educators" JNE, June 1984 Please print or type Name ___________________________________________________ Address ________________________________________________ City_______________________________ ,State________~Zip-----__ ADA MemberMember Number_______ _Nonmember
tains A. the yields of foods in various stages of preparation. B. the nutrient content of foods in various slages of preparation. C. the values for 5 nutrients in selected foods based on yield and retention factors. D. all of the above
Would you like to see JNE offer Continuing Education Articles in future issues? __ Yes __ No Mail this form with check or money order for $10.00 to cover processing to: The Society for Nutrition Education 1736 Franklin Street Oakland, CA 94612 Please circle the correct response to each test question ( 1) A D B C ( 2) A B C D ( 3) A B C D ( 4) A B C D ( 5) A B D C ( 6) B A C D ( 7) D A B C ( 8) A B C D ( 9) A B D C (10) A B D C (11) A B C D (12) A B D C (13) A B C D (14) A B C D (15) A B C D (16) A B C D (17) A B C D (18) A B C D (19) A B (20) A B (21) A B (22) A B (23) A B (24) A B (25) A B
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9.
Which factors should nutritionists consider to more accurately estimate nutrient retention? A. heat lability of nutrients B. water solubility of nutrients C. both A and B D. neither A nor B
10.
Which of the following program features best promotes accurate recipe analysis? A. subroutines to calculate portion size B. capability of storing analyzed recipe information C. procedures to account for nutrient losses D. all of the above
11.
Perloff has developed a series of questions to assist potential data base users in A. coding information to be entered into the computer. B. appraising nutrient data bases. C. determining their nutrient data base needs. D. all of the above
12.
Which of the following can help assure that data bases are accurate? A. competent and knowledgeable nutrient data base developers and managers B. an accreditation process for nutrient analysis programs
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C.
computer tasks to diagnose computational errors D. all of the above 13.
14.
15.
The computerized form of the revised sections of Handbook No.8 are contained in A. Data Set 72-1. B. Nutrient Data Base for Standard Reference. C. Data Set 8-l. D. Handbooks No. 8-1 to 8-10. A nutrient data base contains values from Home and Garden Bulletin No. 72 as well as industry data for infant formulas and vitamin and mineral supplements. Which of the following questions should potential users or purchasers of the data base consider? A. How does the developer handle incomplete data for food items? B. Has the developer used values from a printed or computerized form of Home and Garden Bulletin No. 72? C. Is the data current enough for the user's needs? D. all of the above
B.
avoid using nutrient data from assay techniques rated as conflicting. C. use only standard reference values if a data base will be used by the lay public. D. all of the above
16.
17.
18.
Because laboratory methods for determining the nutrient content of foods are still being researched and refined, nutrition educators need to A. know the state of these methodologies and be prepared for changes.
NUTRIENT The purpose of this program is to make available to schools participating in USDA's National School Lunch Program a nutrient analysis software and data base package that can be used to determine the nutrient content of their school lunches. The software and data base, originally developed by the Consumer Nutrition Division of Human Nutrition Information Service (HNIS) and adapted by the Food and Nutrition Service (FNS) , is designed specifically for school food service. The data base contains approximately 1200 foods that are commonly served in schools and includes the nutrient values for the USDA donated commodities. The nutrient analysis is calculated for eight nutrients: protein, calcium, iron, vitamins A and C, riboflavin, 64
JOURNAL OF NUTRITION EDUCATION
Data bases that store the same food item with varying portion sizes are A. less comprehensive. B. more cumbersome to manage. C. easier to update. D. all of the above Which of the following situations can contribute to entering erroneous data into a data base? A. imputing nutrient values B. frequent updating of the data C. maintaining a relatively large number of food items in the data base D. all of the above Which of the following statements concerning data base nutrient values is FALSE? A. Continuous updating of data bases can be very costly. B. Some educational settings may not require the most recent nutrient values. C. Supplementary data requires frequent updating, but core data does not. D. both A and C
STANDARD
MENU
TRUE/FALSE: For items 19 to 25, select A if the statement is TR UE, and select B if the statement is FALSE. 19.
The term "USDA data" means that a USDA laboratory performed the assay.
20.
The revision of Home and Garden Bulletin No. 72 will include over 4,000 foods and eleven additional nutrients.
21.
The term "data base" can refer to small collections of nutrient data on microcomputer software.
22.
A program which uses a nutrient data base developed from reliable sources ensures an accurate analysis.
23.
The completed revision of Handbook No.8 will consist of over 20 sections.
24.
Enrichment standards have not been changed since 1975.
25.
USDA maintains that data variability is generally as great within a brand as among different brands of the same food.
PLANNING
thiamin, and energy. The software program is available in three languages: FORTRAN, COBOL, and BASIC, to accommodate the variety of hardware (i.e., mainframe, mini-, and microcomputer) used in schools across the nation. The software and data base are currently being tested as part of a larger pilot test called Nutrient Standard Menu Planning (NSMP). The NSMP is a joint effort of USDA's FNS and HNIS. The pilot test was designed and is being managed by the Nutrition and Technical Services Division of FNS. During school year 1983-84, approximately 395 schools across the nation are using the nutrient analysis software and data base to plan menus that meet a nutrient standard (about one-third the Recom-
mended Dietary Allowances) instead of the required USDA meal pattern. The purpose of the NSMP pilot test is to determine whether a nutrient standard method is a feasible option for schools that would like a menu-planning alternative to the school lunch meal pattern. Upon completion and evaluation of the NSMP pilot test in August 1984, USDA will determine whether the nutrient analysis software and data base have been sufficiently tested before making wider distribution of software and data base.
Melody J. Bacha, Nutrition and Technical Services Division, Food and Nutrition Service, U.S. Department of Agriculture, 3101 Park Center Drive, Alexandria, Virginia 22302 VOLUME 16
NUMBER2
1984
Nutrient Data 8asesConsiderations for Educators Loretta W. Hoover1 and Suzanne Pelican 2
JDepartment
of Human Nutrition, Foods, and Food Systems Management, University of Missouri-Columbia, Columbia, Missouri 65211 2Nutrition Program, The Pennsylvania State University, University Park, Pennsylvania 16802
The purpose of this article is to examine the sources and limitations of nutrient data and data bases, and to discuss some educational issues surrounding their selection and use in nutrient analysis programs. LIMITATIONS OF NUTRIENT DATA Properly used, nutrient data can provide valuable information, but educators must be aware of data limitations: potential sources of error, confusion, and/or variability related to the data's dynamic and inexact nature.
State of methodologies. A primary data limitation involves nutrient assay techniques; researchers are still developing and refining laboratory methods for determining many nutrients in foods. Table 1 shows the current state of nutrient methodologies as rated by the USDA Nutrient Composition Laboratory. Nutrition educators might anticipate that methodologies for assaying nutrients such as fiber, trans-fatty acids, and heme iron are rated as "conflicting" or "lacking." However, educators might be surprised that the methodologies for determining food energy, vitamin A, and vitamin C are also rated as "conflicting." To use nutrient data intelligently, educators need to know the state of the various methodologies for determining nutrients. Without such knowledge educators are illprepared to understand and unable to explain to target audiences why changes in data occur as methodologies improve. For example, we would expect to see modification in vitamin C data for some foods with changes in the current methodology. Isoascorbate, a stereoisomer of ascorbic acid, is being used increasingly by the food industry in place of BHA and BHT. Although isoascorbate possesses little, if any, vitamin C biological value, current vitamin C assay procedures do not differentiate between the two compounds. Because isoascorbate in foods can produce false high vitamin C values, 58
JOURNAL OF NUTRITION EDUCATION
USDA is currently working to increase specificity of the vitamin C assay (1, Note 1).
Growing conditions and storage variation. A second limitation is the variability in nutrient content of food items resulting from differences in growing conditions, stage of ripeness, and handling procedures (2, 3). As nutrition professionals, our long-standing awareness of some of these sources of variation may prompt us to gloss over them quickly, but in doing so, we may lose sight of their potential magnitude. Watt points out, for example, that if a potato is stored for six months after harvest, there is a threefold decline in vitamin C content (3). Partly in response to these forms of variability, the Department of Agriculture, in its data base compilation, averages values from several reliable sources for many food items. These averagings are representative or standard reference values reflecting the approximate nutrient content of foods on a year-round, nationwide basis (2, Note 2). As such, these values are useful in the development of educational materials on food composition, in menu planning, in inventory analysis, in dietary analysis of populations, etc.; but use of representative values is not appropriate in situations such as metabolic studies, which require exact values for specific food items. Food preparation. Changes in nutrient content as a result of food preparation can be a third limitation. Educationally, this limitation is important because of the growing commercial promotion of nutrient analysis software for recipe analysis. To accurately estimate a recipe's nutrient content, a program should account for nutrient losses. Although accounting for nutrient losses involves computer programming, we feel the issue of recipe analysis warrants consideration as a data base limitation for two reasons. First, nutrient retention information needed to write such programs is, arguably,
nutrient data, and second, some nutrient analysis software data bases provide compiled values for recipe items. Educators and other software users may need, therefore, to scrutinize nutrient data bases and accompanying programs in terms of the technique used to compute nutrient values from recipes. Although there are several ways to estimate nutrient content of a recipe, Merrill et af. (4, Note 3) report the method based on yield and nutrient retention factors, used to estimate five nutrients in selected foods in the 1963 edition of Agriculture Handbook No.8 (5). USDA has also released a provisional table (6, Note 3) with percent retention values for fifteen vitamins and minerals in approximately eighteen cooked foods (e.g., sweet potatoes, beet) and food groups (i.e., legumes, fruits). Regardless ofthe calculation method used, Agriculture Handbook No. 102 (7, Note 3) is a comprehensive compilation of the yields of foods in various stages of preparation and is useful for estimating changes in weight for ingredients. Educators who use nutrient data bases for recipe analysis should recognize that nutrient retention data is far from complete and that it is difficult, with or without a computer program, to estimate nutrient values from a recipe. Nevertheless, educators can make some gross qualitative distinctions using basic knowledge of heat lability and water solubility of nutrients. For instance, given a calculated nutrient analysis of a cranberry recipe, values for energy and total fat are likely to be more accurate than the values for thiamin and vitamin C because energy and total fat are less affected by heat and water than are thiamin and vitamin C. Rogan and Yu (Journal ofNutrition Education, 16:65-66, 1984) offer some additional examples of variations in nutrient content as a result of food preparation.
Cbanging food supply. A fourth limitation involves data changes resulting from alteraVOLUME 16
NUMBER 2
1984
Table 1
State of development of methods for nutrients in foodb
Nutrient Category
Table 2
State of Methodologya Conflicting
Adequate Substantial
Carbohydrates, fiber, and sugars
Individual sugars
Lacking
Fiber Starch
Energy
Food energy
Lipids
Cholesterol Sterols Trans-fatty acids Fat (total) Fatty acids (common)
Printed Publication Name and Number (Most Recent Publication Date)
Computerized Data Set TapeU and Release Number (Release Date)
Home and Garden Bulletin No. 72 (1981)b
72-1 release 1 (l977)C release 2 (l982)b
Handbook No. 456 (1975)b
456-3 release 1 (l977)C release 2 (l981)C release 3 (1982)C release 4 (1983)b
Handbook No.8 (1%3)b
8-1-0 (l968)C,d 8-1-1 (l972)C Nutrient Data Base for Standard Reference release 1 (1981)C release 2 (l982)C release 3 (l983)b
Minerals/Inorganic Calcium Iron (total) Copper nutrients Selenium Magnesium Phosphorus Potassium Sodium Zinc
Arsenic Chromium Fluorine Iodine Manganese
Proteins and amino Nitrogen acids (total)
Amino acids (most)
Amino acids (some) Protein (total)
Vitamins
Niacin Riboflavin Thiamin Vitamin lUi
Vitamin A Carotenes Vitamin B-12 Vitamin C VitaminD VitaminE Folacin Pantothenic acid
Biotin Choline VitaminK
Handbook No.8 Revised sections 8-/ (1976)b 8-2 (l977)b 8-3 (l978)b 8-4 (1979)b 8-5 (l979)b 8-6 (1980}b 8-7 (1980)b 8-8 (1982)b 8-9 (1982)b 8-/0 (l983)b
Substantial
Conflicting
Lacking
Good
Fair Slow High
aSee Note 4 bCurrently available cNo longer available dReleased on cards, not magnetic tape
Slow
Method modification
Method development! modification Extraction procedures Applications
aDescriplion of methodology states: Adequate Factors Accuracy
Speed of analysis Cost per analysis
Development need5
Excellent Fast Modest
(<$100)
Moderate Modest to high
Extraction procedures Applications
Cobalt Heme-iron Molybdenum Nonheme-iron Silicon Tin Vanadium
Selected USDA nutrient data bases
Poor
Method development Extraction procedures Applications
binformation released by !he Nutrient Composition Laboratory, Agricultural Research Service, U.S. Department of Agriculture, 1983. Table taken from: Beecher, G. R. and J. T. Vanderslice. Determination of nutrients in foods. In Modern methods of food analysis, K. K. Stewart and J. R. Whitaker, ed. Westport, CT: AVI Publishing. (In press)
tions in enrichment standards, the introduction of new foods into the marketplace, and the development of new preparation and processing techniques. These shifts underscore the almost inevitable change in nutrient data, independent of improvements in methodology. For example, the releases of computerized data set 72-1 in 1977 and 1982 (Table 2) reflect different enrichment standards (Note 2). Thus, the release number, which corresponds to the release date, of a USDA data set indicates its currentness and, therefore, its accuracy. One way that the introduction of new foods into the marketplace can contribute to data limitations involves the addition of data for brand name products to a data base. Fast-food data, for example, are common brand name additions to a data base. Although insertion of such data can add specificity and educational utility to a data base, such additions may also introduce VOLUME 16 NUMBER 2
1984
data "gaps" or incomplete data because values may not be available for all of the nutrients included for the original items in the data base. For example, if a data base manager adds a new food item with values for thirteen nutrients but the data base contains values for twenty nutrients for its other food items, then the new food item will have data gaps for seven nutrients. Furthermore, the quality of added data can vary greatly. For instance, some data base developers/managers use USRDA percentages taken from food labels to calculate nutrient values for insertion into a data base. However, such calculations are not appropriate values for most data bases. In conjunction with the question of including data by brand name, the USDA position may interest some data base users: USDA does not usually report nutrient content of foods by brand name because, in most cases, the variability within one brand
is as great as the variability among different brands. Therefore, for those products, values represent the food more accurately when data are averaged across brands (generically) rather than within brands. USDA has, however, reported breakfast cereals by brand name in one of the revised sections of Handbook No. 8 (8) because foods such as breakfast cereals cannot be described generically (9). SOURCES OF DATA BASES USDA. In 1960 and 1977 other researchers (2,10) comprehensively summarized the history and development of many data bases, including those of USDA. Since then, USDA has continued development of its National Nutrient Data Bank (NDB) operated by the Department's Consumer Nutrition Division (CND). The NDB is a computer-based management system that stores nutrient data, but it also includes programs to edit, query, and average individual nutrient values for any given food item (9). The term "USDA data" does not necessarily mean that a USDA laboratory generated the values in question; it means that the reliability of the data has been evaluated by USDA. Although USDA conducts extensive chemical analyses, other sources, such JOURNAL OF NUTRITION EDUCATION
59
as the food industry, university researchers, and other Government laboratories provide much data that are evaluated on the basis of sampling techniques, methodology, replication procedures, etc. This screening of data is a key step in generating representative values that are incorporated into USDA data bases. The NDB is a source of nutrient data in both published and machine-readable or computerized forms (9). Table 2 lists some commonly used USDA nutrient data bases (both printed and computerized) that are either currently available or that have been available and are, therefore, presently in use with many nutrient analysis programs. Several aspects of Table 2 warrant clarification or emphasis. In both Home and Garden Bulletin No. 72 (11) and Agriculture Handbook No. 456 (12) considerable data variations exist between the published and computerized versions. At least two factors contribute to this difference: a) the computerized tapes contain more current data as indicated by the more recent release dates and b) the computerized tapes contain imputed or estimated nutrient values in places where blank spaces appear in the publication (9, Note 2). The variability between printed and computerized versions raises another reference problem: Saying that a given nutrient analysis program uses "USDA data" does not specify the data base. Even identifying a data base as being "No. 72," for example, is inadequate because it is unclear whether the developer transcribed printed values from Home and Garden Bulletin No. 72 to a computerized medium (e.g., floppy disk) or whether the developer purchased a computerized tape from USDA and either used the values directly or transferred the data to another computerized medium. Furthermore, even if the source (i.e., printed form or computerized tape) is apparent, users need to know the ''vintage,'' i.e., the currentness of the data. Thus, when the data base is on computerized tape, developers should indicate the tape's release number and date. A developer using Home and Garden Bulletin No. 72 should also indicate the publication date because it has been revised several times. Table 2 also illustrates the confusion that results from specifying a data base as being "Handbook 8," a phrase we often hear. The publication date of the last complete revision of printed Handbook No.8 (5) is 1963. Although the Handbook is comparatively old, all of the newer data bases contain much of the same data that appears in this 60
JOURNALOFNUTRITIONEDUCATION
1963 edition. However, USDA has undertaken a massive revision to expand Handbook No.8 to include over 4000 foods and to provide data for eleven additional nutrients. The revision includes values for nine minerals, nine vitamins, cholesterol, and amino acids and fatty acids for most foods; data on dietary fiber are also provided for some foods. The revision of Handbook No. 8 is being released in sections; each section reports values of a particular food group. To date, USDA has released ten printed sections (8); the eleventh section, on vegetables, is in press (9). The completed revision of Handbook No. 8 in printed form will consist of over twenty food group sections. The computerized versions of the revision are contained in the Nutrient Data Base for Standard Reference. As new sections of the Handbook are completed, USDA updates this Standard Reference data base and releases a new version identified by a release number and date (Table 2). Until the revision of all twenty food groups is complete, releases of the Standard Reference will be supplemented with older data so that purchasers receive a complete data base with as many current values as are available. As with the other computerized data sets, USDA inserts imputed values for most of the blank spaces in the data base (9). These values are "flagged" on the tape, by means of a code, to alert a user that the data are not based on analytical values (9). Prior to release of revised sections of Handbook No.8, USDA does make some new data available to the public through publication of provisional tables of nutrient composition. These new data may be limited to a given nutrient such as zinc (13), or may cover several nutrients for a group of foods such as beverages. Provisional values are subject to change prior to release of the data in revised sections of Handbook No.8. Other nutrient data bases. In the past two decades, researchers, commercial software firms, and other individuals have compiled expanded nutrient data bases to include foods and nutrients not available in USDA publications (2). These expanded data bases include both print (14) and computerized forms; the current edition of the Nutrient Data Bank Directory (15, Note 5) lists 55 computerized data bank systems, although many other systems exist. Systems that are listed in this directory tend to be extensive data collections with values for many nutrients and different types of food and dietary supplements, and they have widespread applicability ranging from clinical and research
purposes to educational uses. Although we may generally consider a nutrient data base to be a large collection of values, we should not overlook the fact that some educational programs with a specific focus use a small data base. For example, at least one microcomputer program (16) for lay audiences provides analyses only of fast-food items and, therefore, uses a very limited data base. Cautions. In some sense, every nutrient analysis program has its "own" nutrient data base. Realistically, however, most nutrient data bases have evolved from data compiled by USDA (2) with values from other sources added to this USDA "core" (Note 2). This raises the need to emphasize an obvious but frequently overlooked point: Although data from an original source may be sound, manipulation of data (e.g., transferral from original source to computerized medium; routine data base management procedures, etc.) may introduce errors not present in the original source. There is, therefore, no substitute for competent nutrient data base developers and managers. Hoover and Perloff (17, Note 5) have developed several computer tasks to diagnose computational errors in a computer program designed to perform nutritional intake analyses. These exercises can assist in detecting incorrect data although computational errors can also involve improper procedures. Some nutritionists (18) support development of an accreditation process for nutrient analysis software that could help safeguard users against faulty data bases as well as poor programming. DATA BASE SELECTION Tradeoffs. In selecting a nutrient data base, nutrition educators face numerous tradeoffs that vary with instructional goals, the needs of target audiences, and the resource limitations ofthe educational setting. These tradeoffs include data base size or comprehensiveness, completeness, currentness, and sources. Hardware and software associated with a data base are also major considerations, but a full discussion of these as tradeoff factors is beyond the scope of this paper. In terms of data base size and comprehensiveness, a large data base with a wide selection of food items can have advantages. If many specific food items are available, a user is less likely to require food item substitutions when entering dietary intake information. Because substitutions can introduce inaccuracies, a large data base can VOLUME 16
NUMBER2
1984
reduce this source of error. However, as the size of a data base increases, the chances of introducing errors in compiling and managing it increase. Also, if lay audiences use a program directly, they may become frustrated trying to locate items in a large data base. Regarding the issue of size, data base users should note that the number of food item entries in a data base may differ from the number of uniquely described food items because some data bases store the same food item with varying portion sizes. This arrangement can be advantageous educationally if it assists a target audience in specifying portion size. However, the arrangement does not increase the comprehensiveness of the data base, and it increases both data base maintenance responsibilities and the chances for introducing errors. For example, if a single food item appears in a data base three separate times with different serving sizes, the data base manager must make changes in three entries rather than one when updated information is available for the food item. In addition to the quantitative aspects of data base size or comprehensiveness, there is a qualitative aspect regarding the types of foods and nutrients included in a data base. Educators should approach this qualitative issue on the basis of the needs of their settings. For example, a data base that provides values for commercial baby foods might be useful in teaching a nutrition class to new parents. By contrast, such data is of little value to an instructor who uses a nutrient analysis program as part of a weight control class for adults. Educational settings may also require inclusion of specific nutrients. For instance, a nutritionist who supervises a consumer education booth at a shopping mall might feel that sodium and total fat data are essential for effective outreach. The issue of completeness overlaps with data base size and comprehensiveness. As we have indicated, expanding a nutrient data base can create gaps in the data. How these gaps are handled is important to any data base user, including educators. Left unchanged or unmarked, gaps can create analysis errors if the program enters a zero where, in reality, there is a measurable nutrient amount. Therefore, some data base developers and managers choose to flag missing data and/or to impute values. Depending on the type and extent of incomplete data, few educators would want a data base that does not account for data gaps, but there may be tradeoffs between flagging and imputing data. For example, VOLUME16 NUMBER2
1984
an output that flags incomplete data could be confusing to learners. On the other hand, although imputing values may make an output easier to interpret, the values may not be reasonable estimates unless they have been made by a person with substantial knowledge about nutrient data. Although we have repeatedly highlighted the importance of currentness, there are also tradeoffs with this factor. Current values should be accurate, but constant updating also increases the likelihood that erroneous data will enter the system. Continuous updating is also very costly. Furthermore, some educational settings may not require the most recent values. If the primary purpose of an analysis program were, for example, to increase a lay audience's awareness of commonly eaten foods, the program's data base may not need to utilize the most up-to-date values. On the other hand, faculty supervising a graduate program in clinical dietetics may decide that there is a justifiable need to maintain the most current data base. There are also tradeoffs with data base sources. We have stressed the reliability of USDA data, but USDA cannot meet the data needs of all users and all situations. For instance, USDA does not provide data by brand name for fast foods, yet educators often see a need for this information and turn to other sources. Similarly, until the revision of Handbook No. 8 is finished, USDA will not have complete data for the eleven additional nutrients that are being included for the first time. Thus, some data base developers/ managers piece together, from a variety of sources and/or by imputing values, sets of data for nutrients not yet available from USDA for many foods. Although we stress the importance of these added data being reliable, firm judgment of reliability is a virtually impossible task for a potential data base user. In general, however, food companies; published articles; and commercial, government, and university laboratories can provide valuable and usable data. With any source of nutrient data, users should consider limitations such as the state of methodologies for analysis.
Evaluation questions. Perloff (Note 2) developed a series of questions to assist potential data base users in appraising commercial nutrient data bases. As a summary to our discussion, we adapted Perloffs series to produce a set of questions that focuses on the needs of nutrition educators. We stress that the list is a tool to assist in the examination of a data base. It is not complete
for any particular educational setting. Additionally, we refer readers to the discussion by Murphy, King, and Calloway (Journal of Nutrition Education, 16:73-75, 1984) on selecting a nutrient data base and analysis program for use with college students. Our questions to assist with initial evaluation of a data base are as follows: A. Data base core - What data base constitutes the core? - If the core is not from USDA, does the source appear to be reliable? - What is the core's date of release or publication? - Is the release or publication date optimal for your needs? B. Supplementary data - What data, if any, have been added to the core data base? - Do the sources for the supplementary data appear to be reliable? - Do supplementary data include values for nutrients that have methodologies rated as "lacking?" - Are there precautions taken to ensure reliability and accuracy of the supplemental data, and if so, are these precautions adequate for your needs? C. Total data base - Is the data base size (quantitative and qualitative comprehensiveness) optimal for your needs? - Is the data base complete for your needs? - If the data are incomplete, are gaps accounted for in a way that is acceptable, given your needs? D. Data base management - Are all personnel involved with development and management of the data base knowledgeable and competent about nutrient data and data base management? - Is information about the data base core, past updates, and indusion of supplementary data well-documented, readily available, and understandable? D ACKNOWLEDGMENTS
We gratefully acknowledge Betty Perloff for her valuable assistance in preparing this paper. NOTES
I
Personal communication, G. R. Beecher, Chief, USDA Nutrient Composition Laboratory, Building 161, Beltsville Agricultural Center East, Beltsville, MD 20705, February 2, 1984. 2 Perloff, B. Nutrient data bases: Availability, options and reliability. In Proceedings of the JOURNAL OF NUTRITION EDUCATION
61
Eighth National Nutrient Data Bank Conference, R. Tobelmann, ed. Springfield, VA:
U.S. Department of Commerce, National Technical Information Service. (In press) 3 Available from: Nutrient Data Research Branch, Human Nutrition Information Service, U.S. Department of Agriculture, Hyattsville, MD 20782. 4 Available from: National Technical Information Service, U.S. Department of Commerce, 5285 Port Royal Road, Springfield, VA 22161. 5 Available from: Department of Human Nutrition, Foods and Food Systems Management, College of Home Economics, 217 Gwynn Hall, University of MissouriColumbia, Columbia, MO 65211. LITERATURE CITED
1
2
document ARS 62-13, by A. L. Merrill, C. F. Adams, and L. J. Fincher. Washington, DC: 5
6
7
8
National Academy of Sciences. National Research Council. Food and Nutrition Board. Recommended dietary allowances, 9th ed. Washington, DC: National Academy of Sciences, 1980. Hertzler, A. A., and L. W. Hoover. Review of nutrient data bases: Development of food tables and use with computer. Journal of the
American Dietetic Association 70:20-31, 3 4
1977. Watt, B. K. Tables of food composition: Uses and limitations. Contemporary Nutrition 5(2):1-2, 1980. U.S. Department of Agriculture. Procedures
9
for calculating nutritive values of homepreparedfoods. Agriculture Research Service
U.S. Department of Agriculture, 1966, 35 pp. U.S. Department of Agriculture. Composi-
tion of foods-Raw, processed, prepared. Agriculture handbook no. 8, by B. K. Watt and A. L. Merrill. Washington, DC: Government Printing Office, 1963, 190 pp. U.S. Department of Agriculture. Consumer Nutrition Division. Nutrient Data Research Group. Provisional table on percent retention of nutrients in food preparation. Hyattsville, MD, October 1982, 2 pp. U.S. Department of Agriculture. Food yields
summarized by different stages of preparation. Agriculture handbook no. 102, by R. H. Matthews and J. G. Young. Washington, DC: Government Printing Office, 1975. U.S. Department of Agriculture. Composi-
Agriculture, 1984, pp. 292-99. 10 Todhunter, E. N. Food composition tables in the U.S.A. Journal of the American Dietetic Association 37:209-14, 1960. 11 U.S. Department of Agriculture. Nutritive
value offoods. Home and garden bulletin no. 72, Washington, DC: Government Printing
Office, 1981, 34 pp. 12 U.S. Department of Agriculture. Nutritive
value of American foods in common units. Agriculture handbook no. 456, by C. F.
13
14
tion of foods-Raw, processed, prepared. Agriculture handbook no. 8-1: Dairy and egg products, 1976, 144 pp.; 8-2: Spices and herbs, 1977,43 pp.; 8-3: Baby foods, 1978, 231 pp.; 8-4: Fats and oils, 1979,142 pp.; 8-5: Poultry products, 1979, 330 pp.; 8-6: Soups, sauces, gravies, 1980,228 pp.; 8-7: Sausages and luncheon meats, 1980, 92 pp.; 8-8: Breakfast cereals, 1981, 160 pp.; 8-9: Fruits andfruitjuices, 1982,283 pp.; and 8-10: Pork products, 1983, 206 pp., Washington, DC:
17
Government Printing Office. Rizek, R. L., F. N. Hepburn, and B. P. Perloff. USDA's nutrient data bases. In Proceedings of the 1984 USDA Agricultural Outlook Conference, October 31-November 3, 1983. Washington, DC: U.S. Department of
Columbia Printing Services, 1981, 76 pp. 18 Sorenson, A. W., R. Seltser, and B. Wyse. Personal computers for health. The Professional Nutritionist 15(1):1-3,6, 1983.
15
16
Self-Assessment Question nai re After reading the Continuing Education Article, "Nutrient data bases-Considerations for educators," please answer the following questions by indicating your responses on the Self-Assessment Questionnaire Answer Form located on the next page. Registered dietitians should return the completed form, with a check for $10.00 to cover processing, to The Society for Nutrition Education, 1736 Franklin Street, Oakland, CA 94612. This activity has been approved for one hour of Continuing Education Credit by the Commission on Dietetic Registration. Answers to the selfassessment questionnaire can be found on page 76. Questionnaires must be returned within six months of their appearance in the Journal in order to be eligible for credit. Notification will not be sent if hour is approved.
MULTIPLE CHOICE: For items 1 to 18, select the ONE best answer. 1. Which of the following is NOT true? A. Conflicting methods exist for assaying fiber and trans-fatty acids. 62
JOURNAL OF NUTRITION EDUCATION
Adequate assay methods have been developed for both vitamin A and C. C. The assay methods for vitamin K and biotin are lacking. D. both A and C
2.
Which of the following variables have prompted USDA to average values for many food items? A. growing conditions of crops B. stage of crop ripeness C. food handling procedures D. all of the above
3.
Representative values generated by USDA reflect the approximate nutrient content of A. foods available year-round across the nation. B. similar types of foods. C. a standard or key food in a food group. D. commonly eaten foods. The representative values calculated by USDA are NOT useful in A. dietary analysis of populations.
review of nutrient data base system capabilities. Columbia, MO: University of Missouri-
B.
educational materials on food composition. C. metabolic studies. D. inventory analysis.
B.
4.
Adams. Washington, DC: Government Printing Office, 1975, 291 pp. Murphy, E. W., B. W. Willis, and B. K. Watt. Provisional tables on the zinc content of foods. Journal of the American Dietetic Association 66:345-55, 1975. Pennington, A. T., and H. N. Church. Food values of portions commonly used. New York, NY: Harper and Row Publishers, 1980, 186 pp. Eighth National Nutrient Data Bank Conference. Nutrient data bank directory, by L. W. Hoover, T. Fisher, and D. Hay. 3rd edition. Columbia, MO: University of MissouriColumbia Printing Services, December 1983, 21 pp. Schrank, J. Fast food micro-guide. The Learning Seed, Kildeer, IL, 1983. Hoover, L. W., and B. P. Perloff. Modelfor
5.
Which of the following is NOT true concerning name brand data? A. Food labels provide data that is sufficiently exact for most data bases. B. Brand name data can add specificity and educational utility to a data base. C. Brand name data may not be available for all of the nutrients included for other foods in a data base. D. both Band C
6.
Which of the following is TRUE concerning USDA and nutrient data for name brand foods? A. USDA does not report any nutrient values for name brand foods. B. USDA reports name brand nutrient values for breakfast cereals. C. USDA generally averages nutrient values across brands of the same food. D. both B and C VOLUME16
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7.
Which of the following reasons explains the variations in nutrient data between the published and computerized forms of Handbook No. 456? A. The computerized tapes contain more current nutrient data. B. The computerized tapes contain some estimated nutrient values when exact values are not known. C. Nutrient information on a greater number of foods can be stored on the computerized form. D. both A and B
8.
Agriculture Handbook No. 102 con-
SELF-ASSESSMENT QUESTIONNAIRE ANSWER FORM Expiration Date: December 1984 Continuing Education Article "Nutrient data bases - Considerations for educators" JNE, June 1984 Please print or type Name ___________________________________________________ Address ________________________________________________ City_______________________________ ,State________~Zip-----__ ADA MemberMember Number_______ _Nonmember
tains A. the yields of foods in various stages of preparation. B. the nutrient content of foods in various slages of preparation. C. the values for 5 nutrients in selected foods based on yield and retention factors. D. all of the above
Would you like to see JNE offer Continuing Education Articles in future issues? __ Yes __ No Mail this form with check or money order for $10.00 to cover processing to: The Society for Nutrition Education 1736 Franklin Street Oakland, CA 94612 Please circle the correct response to each test question ( 1) A D B C ( 2) A B C D ( 3) A B C D ( 4) A B C D ( 5) A B D C ( 6) B A C D ( 7) D A B C ( 8) A B C D ( 9) A B D C (10) A B D C (11) A B C D (12) A B D C (13) A B C D (14) A B C D (15) A B C D (16) A B C D (17) A B C D (18) A B C D (19) A B (20) A B (21) A B (22) A B (23) A B (24) A B (25) A B
VOLUME 16 NUMBER 2
1984
9.
Which factors should nutritionists consider to more accurately estimate nutrient retention? A. heat lability of nutrients B. water solubility of nutrients C. both A and B D. neither A nor B
10.
Which of the following program features best promotes accurate recipe analysis? A. subroutines to calculate portion size B. capability of storing analyzed recipe information C. procedures to account for nutrient losses D. all of the above
11.
Perloff has developed a series of questions to assist potential data base users in A. coding information to be entered into the computer. B. appraising nutrient data bases. C. determining their nutrient data base needs. D. all of the above
12.
Which of the following can help assure that data bases are accurate? A. competent and knowledgeable nutrient data base developers and managers B. an accreditation process for nutrient analysis programs
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C.
computer tasks to diagnose computational errors D. all of the above 13.
14.
15.
The computerized form of the revised sections of Handbook No.8 are contained in A. Data Set 72-1. B. Nutrient Data Base for Standard Reference. C. Data Set 8-l. D. Handbooks No. 8-1 to 8-10. A nutrient data base contains values from Home and Garden Bulletin No. 72 as well as industry data for infant formulas and vitamin and mineral supplements. Which of the following questions should potential users or purchasers of the data base consider? A. How does the developer handle incomplete data for food items? B. Has the developer used values from a printed or computerized form of Home and Garden Bulletin No. 72? C. Is the data current enough for the user's needs? D. all of the above
B.
avoid using nutrient data from assay techniques rated as conflicting. C. use only standard reference values if a data base will be used by the lay public. D. all of the above
16.
17.
18.
Because laboratory methods for determining the nutrient content of foods are still being researched and refined, nutrition educators need to A. know the state of these methodologies and be prepared for changes.
NUTRIENT The purpose of this program is to make available to schools participating in USDA's National School Lunch Program a nutrient analysis software and data base package that can be used to determine the nutrient content of their school lunches. The software and data base, originally developed by the Consumer Nutrition Division of Human Nutrition Information Service (HNIS) and adapted by the Food and Nutrition Service (FNS) , is designed specifically for school food service. The data base contains approximately 1200 foods that are commonly served in schools and includes the nutrient values for the USDA donated commodities. The nutrient analysis is calculated for eight nutrients: protein, calcium, iron, vitamins A and C, riboflavin, 64
JOURNAL OF NUTRITION EDUCATION
Data bases that store the same food item with varying portion sizes are A. less comprehensive. B. more cumbersome to manage. C. easier to update. D. all of the above Which of the following situations can contribute to entering erroneous data into a data base? A. imputing nutrient values B. frequent updating of the data C. maintaining a relatively large number of food items in the data base D. all of the above Which of the following statements concerning data base nutrient values is FALSE? A. Continuous updating of data bases can be very costly. B. Some educational settings may not require the most recent nutrient values. C. Supplementary data requires frequent updating, but core data does not. D. both A and C
STANDARD
MENU
TRUE/FALSE: For items 19 to 25, select A if the statement is TR UE, and select B if the statement is FALSE. 19.
The term "USDA data" means that a USDA laboratory performed the assay.
20.
The revision of Home and Garden Bulletin No. 72 will include over 4,000 foods and eleven additional nutrients.
21.
The term "data base" can refer to small collections of nutrient data on microcomputer software.
22.
A program which uses a nutrient data base developed from reliable sources ensures an accurate analysis.
23.
The completed revision of Handbook No.8 will consist of over 20 sections.
24.
Enrichment standards have not been changed since 1975.
25.
USDA maintains that data variability is generally as great within a brand as among different brands of the same food.
PLANNING
thiamin, and energy. The software program is available in three languages: FORTRAN, COBOL, and BASIC, to accommodate the variety of hardware (i.e., mainframe, mini-, and microcomputer) used in schools across the nation. The software and data base are currently being tested as part of a larger pilot test called Nutrient Standard Menu Planning (NSMP). The NSMP is a joint effort of USDA's FNS and HNIS. The pilot test was designed and is being managed by the Nutrition and Technical Services Division of FNS. During school year 1983-84, approximately 395 schools across the nation are using the nutrient analysis software and data base to plan menus that meet a nutrient standard (about one-third the Recom-
mended Dietary Allowances) instead of the required USDA meal pattern. The purpose of the NSMP pilot test is to determine whether a nutrient standard method is a feasible option for schools that would like a menu-planning alternative to the school lunch meal pattern. Upon completion and evaluation of the NSMP pilot test in August 1984, USDA will determine whether the nutrient analysis software and data base have been sufficiently tested before making wider distribution of software and data base.
Melody J. Bacha, Nutrition and Technical Services Division, Food and Nutrition Service, U.S. Department of Agriculture, 3101 Park Center Drive, Alexandria, Virginia 22302 VOLUME 16
NUMBER2
1984