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A major consideration in developing NUTREDFO was to obtain the most accurate nutrient and food constituent values possible. For those nutrients in foods for which analytical values existed the objective was to determine the most appropriate original source of data. For nutrient values for which analytical data did not exist, the most legitimate criteria for imputation had to be identified.
Table 1. NUTREDFO system functions for analytical testing
1. List nutrient and food constituent levels for any food both in the permanent and temporary data file (this function lists any of the 26 nutrients, food constituents, and serving size specified by the user).
2. List foods in rank order by nutrient or food constituent based on contribution of that nutrient or constituent per serving of the food item.
3. List foods in rank order for any specified nutrient based on the percentage of standard for that nutrient. Any one of three standards can be used in this function. including the 1980 RDA , Single-value Nutrient Allowances per 101)0 Kilocalories  or the US Recommended Daily Allowance .
4. List foods in rank order for a user-specified nutrient based on its Index of Nutritional Quality (INQ) . This rank-ordering uses the same standards as in those in no.3 above.
5. Calculate the mean, standard deviation of the mean, and minimum/maximum range for selected nutrients in user-specified groups of foods.
6. Allow the user to temporarily change serving sizes and nutrient and food constituent levels in both the permanent and temporary data files. This function can be used to examine the effects of changes in nutrient levels on selected foods, but does not jeopardize the security of data in either file.
7. Calculate the nutrient value of menus. Menus can be for individual eating occasions, individual days, and for a group of days.
8. Calculate the Index of Nutritional Quality; percentage of standard for nutrient totals, percentage of calories from total protein, carbobydrate, fat, and alcohol; and calculate values for each nutrient and food constituent per 1000 kilocalories for each eating occasion and each day.
9. Calculate the mean, standard deviation, and minimum/maximum range for each nutrient and food constituent in a multi-day grouping of menus.
USDA was considered the most appropriate original source of data for raw or cooked foods. Data values available in recently revised sections of Handbook No.8[2, 14, 22, 23,30,41, 43,44,57,58,59, 60] were used preferentially, and then provisional food composition tables developed by USDA [13, 25, 26, 45] and values in the 1963 edition of Handbook No.8 . All values were verified with the original source. Whenever possible, analytical iron values from Iron Content of Foods  were used for items not listed in revised Handbook No.8 sections.
USDA computerized data tapes [64,65,67] were used to supply data for missing values in Handbook No.8 printed tables. A recent article by Hepburn  discussed these data tapes. In some cases, particularly for vitamins B6, and B12, published or computerized values had to be recalculated because retention and cooking yield factors had been inconsistently applied .
Journal articles by USDA research specialists [3,4,5,6,9,16,17,18,19,20, 21,37,38,47, 53, 55,56, 72] and other USDA publications [42,48,52] were also used as sources for data for specific nutrients and food constituents not available in Handbook No. 8, the provisional tables, or the data tapes. These nutrients include fatty acids, cholesterol, zinc, folacin, and sugars in ready-to-eat and granola cereals. Occasionally, food composition sources [8,49,70] not published by USDA provided data where none could otherwise be found.
Nutrient values were imputed when acceptable published sources were not available. Imputations were generally derived through mathematical manipulation or adjustment of data from published sources. The preferred approach was to calculate nutrient values for cooked products using data for raw products and applying USDA retention and yield factors. When this was not possible, data values were assumed from similar products, e. g. values for cooked chard were used to estimate values for cooked romaine. An additional recourse was to consult experts in the field, usually Consumer Nutrition Center specialists who were working on particular food and nutrient data and who had access to industry and other data specialists. Calculations for recipe imputations were based on published USDA procedures as used in Handbook No. 8 .
The NUTREDFO system provides specific information about the source for each nutrient and food constituent value. This documentation enables users to have on-line access to the source of every nutrient value in the data base.
Many factors influence the reliability and accuracy of nutrient composition data, and an understanding of these factors is essential before working with nutrient data bases. Recently, the strengths and limitations of nutrient data have been addressed in detail in conferences, speeches, and papers [31,51,62]. Of special interest are discussions by Perloff  and Hepburn  on USDA food composition data. Their papers discuss the uses, strengths, and limitations of food composition data in general. Four issues about food composition data are particularly important: (a) the representativeness of the data; (b) the sources of data; (c) the method used to derive nutrient values; and (d) the adequacy of analytical methodologies.
The representativeness of data depends on the number and quality of laboratories performing food composition analyses, the number and quality of samples used, and any weighting procedures used. Representative data reflect the nutrient composition of food products on a nationwide, year-round basis. Perloff  suggests that data are generally more reliable when they are based on analyses of a large number of samples from many locations and when they are compiled from several laboratories.
Often, a weighting scheme is used to average nutrient values from a number of samples. This makes nutrient values more representative of a national food supply and allows varieties of foods that are produced and consumed in larger quantities to be more accurately represented in the final value.
The reliability and usefulness of data also depend on its source. Analytical procedures and methods differ between laboratories and can influence the reliability and accuracy of nutrient information. This underlines the importance of using data compiled from several sources .
Published food composition values are derived either through direct analysis or by calculation. When data are obtained from the direct analysis of samples, natural variations in a food sample should be considered. Techniques for analysing samples also change with changes in prevailing cultivars or breeds, food products, and advances in food technology .
Values that cannot be averaged from actual analytical data can be calculated using analysed values. For example, protein is calculated from the nitrogen content of the food. Calculations for food mixtures are performed using analytical values for ingredients in the mixture. For cooked foods, calculations are based upon analytical values for the raw products, then adjusted for yield and retention factors.
Calculated values provide a different kind of data than those from direct analysis. The quality of calculated values depends on the quality of the original analytical values and the accuracy of calculation procedures. As advances are made in analytical methodologies, truer measurements of nutrient levels will result in better quality and greater reliability of data from both direct analysis and calculations .
The state of analytical methodology is an important factor influencing the availability and quality of food composition data. Hepburn  suggests that the status of analytical methodologies is often dependent on the interest expressed by experts in the field as well as on advancements in technology. Interest in certain nutrients shown by the professional community often sparks further research in the development of analytical methods for those nutrients.
The NUTREDFO data base was specifically designed for use by professionals as a tool for nutrition guidance research, and for the development of nutrition guidance information and other technical nutrition information. Throughout the world the means by which we have communicated nutrition generally to the population has been a food grouping system . In the United States, the Department of Agriculture has traditionally developed food guides for the purpose of translating dietary allowances into a form which the consuming public can use to improve the nutritional balance of their diets. USDA food guides have evolved over time as a result of an increased understanding of human nutrient needs, food composition, and the relationship of diet to health . All the guides have emphasized that maintenance of good health depends upon consuming a varied diet that will provide adequate amounts of energy and essential nutrients.
The Basic Four Food Groups system, which is currently used in the United States , is based upon the balance concept, which assumes that an appropriate mixture of food items from each group will form the foundation of an adequate diet with respect to protein and certain vitamins and minerals for which dietary standards and adequate food composition data were available at the time of the plan's inception. Nutritionists designed the guide to provide approximately 1,200 kcal and at least 80 per cent of the eight nutrients which had Recommended Dietary Allowances (RDA) published in 1953 . These nutrients were protein, vitamin A, thiamine, riboflavin, niacin, vitamin C, calcium, and iron. Because of dietary inadequacies in calcium and vitamins A and C in the American population at that time, the food sources of these nutrients were emphasized - thus the formation of dairy and fruit and vegetable groups. Protein was also cited for specific attention in the meat group because diets containing animal protein sources were expected to contribute micro-nutrients that were difficult to obtain in sufficient amounts from other foods.
This food grouping system assumed that if the need for the key or "leader" nutrients (the basis of the four food groups) were met, then it was likely that requirements for other nutrients such as vitamins B6, B12. magnesium, zinc, folic acid, and pantothenic acid would also be satisfied . As new findings are reported on the functions of these other nutrients; as methodologies improve for quantifying these nutrients in foods and biological tissues; and as we examine current food consumption practices, and evaluate and develop diets and menus based on new data, indications are that the assumption behind food commodity groupings and so-called leader nutrients may no longer be valid.
In addition, diseases caused by deficiencies of leader nutrients are currently not the major nutritional concerns in the United States. Other diseases such as cancer, diabetes, hypertension, and heart disease have developed despite the widespread use of the food-group concept in nutrition education. The recent addition by the USDA of a fat, sugar, and alcohol group to the Basic Four addresses these concerns by helping the public become more aware of the dietary levels of nutrients that have been linked to public health problems.
In 1980, USDA and the Department of Health, Education and Welfare (now Health and Human Services) jointly issued recommendations entitled Dietary Guidelines for Americans . As with other dietary guidance materials, the guidelines are designed to help consumers make informed choices about foods. The object is to obtain the correct balance of vitamins, minerals, and dietary fibre, without overconsuming salt or calories, especially calories from fat, sugar, and alcohol.
An important companion publication to the Dietary Guidelines is Ideas for Better Eating: Menus and Recipes to Make Use of the Dietary Guidelines . The menus are designed for healthy adults. There are two versions of each day's menus, one providing 1,600 kcal and the other 2,400 kcal, reflecting the average amounts of energy from foods that women and men, respectively, reported consuming in recent food consumption surveys . Thus, these menus are designed not for weight reduction, but to reflect as closely as possible the recommendations in the Dietary Guidelines and RDA. The menus on average contain less than 35 per cent of calories as fat (as suggested in the text of the 1980 RDA), 50 per cent or more calories from carbohydrates, an average cholesterol content lower than the current average consumption, and sodium content within the 1,100 to 3,300 milligrams range recommended in the 1980 RDA publication.
The NUTREDFO data-base system was developed to analyse the menus in Ideas for Better Eating. A careful analysis of these menus indicated a number of problems. The protein content, and most vitamin and mineral content, varied between menus, but on average met or exceeded the 1980 RDA for adults. The exceptions were vitamin B6, folacin, iron, and zinc on the 1,600 kcal diet. Even though these values were low in the menus, they were not as low as usual consumption levels reported at a comparable level of calories in national surveys.
The advances that have been made in improving analytical data for a greater number of nutrients have identified new nutritional problems which, it seems, current nutritional guidance is not addressing or the food supply adequately providing. Nutritional guidelines must reflect current dietary concerns, which in part result from advances in food composition research. The question for nutrition education is, should we change the configuration of the food grouping system to emphasize the nutrients that are now of concern?
During the past few years, several articles have criticized commodity-based food grouping systems [7, 27, 35, 50]. Critics argue that food groups fail to assure nutritional adequacy and are of little relevance to current nutritional thinking. Some have suggested replacing commodity-based systems with nutrient-based systems, thus assuring individuals of a closer approximation to dietary standards .
However, commodity food groups have long been used as a basis for nutrition education; they are natural and easily recognized by people with little technical background in nutrition [1, 36]. Nevertheless, the variability of nutrient compositions within a commodity group can lead to inaccurate or misleading nutritional information. A summary of food composition that is based on both commodity groups and similarity in nutrient attributes can be very useful in understanding and explaining the nutritional structure of the food supply.
Using NUTREDFO food composition data we applied mathematical clustering algorithms to the classification of foods within commodity groups into subgroups or clusters with similar nutrient compositions . The results obtained depend on the nutrients selected. By using nutrients that have limited availability in the food supply (i.e. vitamin B6, calcium, iron, magnesium, folacin, and zinc) and those that pose a possible increased health risk (i.e. sugar, fat, cholesterol, and sodium), it is possible to identify quickly those foods that provide adequate amounts of essential nutrients and excessive amounts of nutrients of concern. Furthermore, the clustering algorithm overcomes a problem that has made it difficult in the past to group foods objectively and accurately, namely, that of dealing simultaneously with more than one or two nutrients. As many nutrient attributes as desired can be used and analysed simultaneously by the algorithm.
The algorithm also provides a "cluster centre" or prototype nutrient composition which represents a summary of the nutrients in the foods assigned to a cluster. Figure 1 illustrates clusters for dairy foods: low-fat milks, plain yoghurt, and buttermilk clustered together owing to their high nutrient density and lowest amounts of total and saturated fat and sugar, and low cholesterol contents (fig. la); whole milk and natural cheeses grouped together due to their moderate nutrient levels and relatively high fat and sodium contents (fig. 1b); both creamed and low-fat cottage cheese clustered together (fig. 1d), with their high sodium content overriding the differences in fat content of these two products. Subgroups based upon similarities in attributes were also identified in other food commodity groups .
The results obtained indicate that this technique provides valuable insight into the nutrient composition of the food supply. Many of the clusters obtained were ones that might have been anticipated. However, some unexpected associations occurred, which, when seen, were quite logical, but would probably not have been predicted. Moreover, in some commodity groups the cluster centres indicate that although the amounts of certain nutrients may vary from one cluster to another they tend to occur in the same proportion. This means that further investigation could lead to a system of "leader" nutrients, those whose presence indicates the presence or absence of other nutrients. The development of the expanded NUTREDFO data base was and will continue to be critical to furthering our research in the area of dietary guidance and nutrition education.
Nutrient density characterization of five dairy group clusters. Food items listed are those
Fig. 1. Nutrient density characterization of five dairy group clusters. (a) Cluster 1: I per cent milk; 2 per cent milk; skim milk; buttermilk; low-fat plain yoghurt.
Fig. 1. Nutrient density characterization of five dairy group clusters. (b) Cluster 2: Whole milk; natural cheese.
Fig. 1. Nutrient density characterization of five dairy group clusters. (c) Cluster 3: Chocolate milk, whole and 2 per cent; yoghurt, flavoured and fruited.
Fig. 1. Nutrient density characterization of five dairy group clusters. (e) Cluster 5: Processed cheese; Parmesan cheese; blue cheese; cheese spread.
Fig. 1. Nutrient density characterization of five dairy group clusters. (d) Cluster 4: Creamed cottage cheese; cottage cheese, I per cent fat.
NUTREDFO users should note that data for some nutrients and food constituents are severely limited or subject to question due to the lack of a standardized methodology. These nutrients are folacin, pantothenic acid, zinc, and "added sugar." Caution should be used when basing nutrition guidance decisions on levels of these nutrients or when reporting these levels in menus, food plans, or other information materials.
Folacin values have been published for only a few forms of various foods. Imputations were necessary to apply these generalized values to specific NUTREDFO foods. In addition, there is conflicting information about the analytical methodology to be used for folacin [62, 63].
Pantothenic acid is also a nutrient for which available methodology provides conflicting information [62, 63]. Although some studies have been conducted to analyse pantothenic acid values, data for processed and prepared foods are still limited , and it was necessary to impute levels in many foods.
Published data for zinc are very limited. A 1975 article by Murphy et al.  provided data for some important levels of zinc and for widely consumed foods as reported in USDA's 1965/66 Household Food Consumption Survey .
Because of the interest in added sugar in the diet, estimates were included in the NUTREDFO system. These values represent the grams of carbohydrate found in excess of those naturally occurring in the food. All "added sugar" values, except ready-to-eat (RTE) cereals, were imputed. RTE breakfast cereals have been analysed for their total sugar content [37, 38]. Since RTE cereals with no added sugar have less than one gram of sugar per 100 grams, total sugar values were used.
Although data on the levels of magnesium, and vitamins B6 and B12, were reported in the 1977/78 Nationwide Food Consumption Survey (NFCS) , data for some foods are limited. In addition there is conflicting information about analytical methodology for vitamin B12 [63, 68]. Care should be exercised when interpreting information about these nutrients.
Sodium values in the NUTREDFO permanent data base represent only naturally occurring sodium and sodium added during processing and in recipes. Table salt and salt added during preparation at home are not included. In response to consumer demand for products containing less sodium, some companies are lowering the levels of salt and sodium-containing compounds in products that have traditionally provided substantial amounts of dietary sodium [40, 54, 61]. Consideration will be given to including these products in the NUTREDFO nutrient data file if they become a significant percentage of market-place sales. The NUTREDFO software program has the capability of allowing users to temporarily adjust nutrient values. This capability can be used to test the impact of these new retail products in menu planning or other activities.
As a result of our experiences in developing data bases and putting them to innovative uses, a number of recommendations can be made regarding food composition data and food data systems. We will want more and better data. However, when setting priorities it is important to give first consideration to analysing foods that are representative of current food consumption practices. In addition, it is essential to provide documentation about the source of analytical, imputed, and averaged values to use in resolving discrepancies when they occur. With respect to systems development, in whatever way data are brought together they need to be in a manageable form that is amenable to various types of software. Even a relatively small data base of about 500 foods and 30 nutrients presents difficulties as regards the technical development of software for data use. Something as seemingly straightforward as assigning fields for analysing data can be difficult when systems developers are unaware, for example, of extremes of data values such as vitamin A in liver. While this may seem too simplistic to mention, a great amount and variety of software will be developed for using data in many different ways. Systems developers need to be thinking of fairly sophisticated uses and potential uses for data as they begin to put together a data base.
Supported in part by USDA Co-operative Agreement 58-3198-2-85 and Utah Agricultural Experiment Station Project 758-3. Journal paper no. 315 l of the Utah
Agricultural Experiment Station, Utah State University, Logan, UT 84322; approved by the Director.
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