This Session was chaired by Ms Karen Cashel of the University of Canberra. A keynote address was presented by C.E. West entitled The Future Information Needs for Research at the Interface between Food Science and Nutrition. This was followed by papers on Food Database Management Systems—a Review by W. Becker and I. Unwin and Data Identification Consideration in International Interchange of Food Composition Data by J.C. Klensin. These papers were followed by a computer demonstration Food Data: Numbers, Words and Images by B. Burlingame, F. Cook, G. Duxfield and G. Milligan. These are all published in this Section.
The following posters Computer Construction of Recipes to Meet Nutritional and Palatability Requirements by L.R. Fletcher and P. Soden (presented by D.A.T. Southgate) and Requirements for Applications Software for Computerized Databases in Research Projects by D. Mackerras are published at the end of this Section.
Clive E. West
Department of Human Nutrition, Wageningen Agricultural University,
PO Box 8129, 6700 EV Wageningen, The Netherlands
Program Against Micronutrient Malnutrition, Center For International Health, Emory University School of Public Health, 1518 Clifton Road, NE, Atlanta GA 30322, USA
Nutrition and food science are disciplines at the interface between agriculture and health. Therefore, their information needs encompass both those of agriculture and health, and in addition extend into the realms of other disciplines such as the basic physical sciences, mathematics, the social sciences from economics to anthropology, the behavioral sciences, and history. In this paper, attention will be directed to the narrow interface between nutrition and food science, addressing information needs such as food naming and description, food intake, attributes of foods, and nutritional status.
In order to be certain about the identity of foods being consumed or traded, general agreement about food names is needed backed up by an adequate food description system containing a sufficient number of terms to describe foods in an unambiguous way. For this purpose, several systems, or types of systems, have been developed including Langual (1) the INFOODS system (2) and Eurocode 2 (3) as discussed more fully by Pennington (4).
The most important use of Langual in Europe and in other regions outside the United States may not be in its comprehensive use but in the series of definitions which it provides for the description of food attributes. In order to ensure that the Langual system being used in Europe does not develop independently of that continuing to be developed by the Food and Drug Administration, a joint USEuropean Committee has been established. The long-term success of Eurocode 2 and/or Langual in Europe depends on adoption in major Europeanwide epidemiological studies and not just on endorsement by projects such as FLAIR Eurofoods-Enfant.
• Food Intake
It is possible to measure food consumption or intake at three levels: the national level using food balance sheets; at the household level using household budget surveys; and at the individual level using individual food consumption surveys. The data obtained from these approaches enable the availability or consumption of foods, and therefore of nutrients, to be monitored. They can be used for a variety of purposes such as the development and monitoring of agricultural, food and nutrition policies and for studying the relationship between diet and health. The three approaches for measuring food intake are complementary since all have their advantages and disadvantages. At all levels, challenges are emerging.
National and Regional Level
Food balance sheets provide a picture of food disappearance within a country during a specified reference period. The term “food disappearance” refers to “food available for human consumption” and not to “food actually eaten”. It can be calculated not only for the whole population but also on a per capita basis by dividing the quantity of food by the population. Food balance sheet data are useful in monitoring trends in food consumption over time and in making rough comparisons between countries. Often, such data are the only data which can be readily obtained for rapid evaluation of new problems. The continued need for such data was highlighted by the resolutions of the International Conference on Nutrition held in Rome in December 1992 (5). Countries attending gave a commitment to meet the Nutrition Goals of the Fourth United Nations Development Decade:
to eliminate starvation and death caused by famine
to reduce malnutrition and mortality among children substantially
to reduce chronic hunger tangibly
to eliminate major nutritional diseases.
The first three goals are directed essentially to problems in developing countries, while eliminating nutritional diseases also refers to the problems associated with the excess consumption of particular foods and nutrients. Food balance sheets will be one instrument in monitoring the food and nutrition situation in countries throughout the world and reacting to it. There are a number of challenges associated with the provision of food balance sheet data which are peculiar to various areas of the world.
European Union. Ways have to be found to collect data, at the national level in countries in the European Union after the creation of the single market. Traditionally, national food balance data have been compiled largely from data collected for customs purposes. However, with the creation of the single market, customs data are no longer available. Unfortunately, this has also come at a time when statistical offices in Europe are undergoing reorganization and budget cuts. The problem will be exacerbated with the enlargement of the European Union. The FLAIR Eurofoods-Enfant Project has held discussions with the three organizations responsible for publishing food balance sheets up until this time: with Eurostat, which is the Statistical Office of the Commission of the European Union, FAO and with OECD. The purpose of the discussion is to explore whether other survey techniques can be used to complement or even replace the data collected in the conventional food balance sheets. FAO and OECD are keen to maintain food balance sheets but Eurostat will provide data only for the countries of the European Union as a whole because other Directorates in the Commission have no interest in food intake in individual countries in Europe. This is because the Commission has no direct mandate for health and nutrition matters but only an indirect mandate through its involvement in social issues. Thus the interest of the Commission in food intake will probably be restricted to the household level. It remains to be seen whether FAO and OECD can continue to collect food balance sheet data for the countries of the European Union.
Eastern Europe. The increasing number of newly emerging countries of Eastern Europe and the established countries in transition have an even more pressing problem in providing data on food intake at the national level. Many surveys have shown that the amount of food available in these countries is declining rapidly but the data available to monitor such changes are often poor and not comparable over time or among countries. The situation is exacerbated by the lack of infrastructure for the collection, analysis and dissemination of the data. OECD is providing help to many countries in the region to improve the provision of such data but more needs to be done. This is important in order to maintain stability in the countries and for making international arrangements concerning trade and external assistance.
Developing Countries. In developing countries, especially in Africa where the per capita availability of food remains low, there is a continuing need to collect data. However, often the data are of low quality because of the inherent problems in collecting and analyzing information on food provided through non-commercial channels such as that produced at the household level or obtained by hunting, gathering or fishing. FAO and a number of governments provide assistance to some countries to improve their data collection and analysis procedures and capabilities. However, more needs to be done especially because of the need to plan external assistance when the food situation in countries deteriorates.
Coordination. In addition to maintaining and improving the collection, analysis and dissemination of data from food balance sheets, there should be more coordination of other surveys designed to build up a picture of food consumption at the national level. Such data are often collected at the household or individual level. As mentioned later, there is a need to improve the quality of food composition data associated with food balance sheets.
At the household level, there are three main challenges: to improve the quality of the data on food purchases generally; to obtain comparability between countries; and to determine food consumption outside the home. Household budget surveys were designed to measure household expenditure, often for determining retail price indices, and not to measure food intake for nutritional purposes. Although household budget surveys are coordinated among countries of the European Union, the scope for improving the usefulness of data collection for nutritional purposes is somewhat limited because of the priorities of those collecting the data, the need to maintain the comparability of the data over time, and the problem of converting expenditure on food to food consumption. As yet, surveys designed to measure food purchases at the household level are not coordinated within Europe or among other countries. This is unfortunate because, for nutritional surveys, household food surveys will provide better data on household food consumption than will household budget surveys which are designed with another function in mind. However, unless those who wish to coordinate national household food surveys can come up with money to improve or modify surveys, there is very little chance that national household food surveys will be coordinated in the foreseeable future. Measuring food consumption outside the home is very difficult because the person responsible for purchasing food for the family as a whole is often not aware of food purchases by individual household members.
Data at the individual level, particularly if for a sufficient number of people, provide the best information for nutritionists especially for examining the relationship between diet and health or diet and disease. It is not appropriate to discuss here all that needs to be done to improve dietary intake information at the individual level as this topic has been discussed in detail at other meetings such as the Dietary Assessment Meetings, the first of which was held in 1992 (6). However, a number of points should be noted. Firstly, there should be more coordination to improve consistency of data among countries. This can only be achieved when the coordinating agency can offer funds to those carrying out the work. Otherwise countries are reluctant to change their systems because such changes can affect the continuity of the data. Secondly, countries not collecting data at the individual level should be encouraged to do so. This will enable countries to compare themselves with each other. Any coordination will probably have more effect on new surveys than on those already established such as the National Food Survey in the UK (7) and in the food intake components of the NHANES surveys in the US (8). Thirdly, more attention should be paid to collecting data required to answer such research questions as the bioavailability of nutrients and the etiology of cancer and other diseases.
• Food Composition
One of the first priorities when INFOODS was established (9) was to produce guidelines for the production, management and use of food composition data. These guidelines (11), which were published with the assistance of the FLAIR Eurofoods-Enfant project, have now become the definitive work in the area. However, for nutrition research, there are a number of problems which need to receive increased attention in the future.
Data on More Foods
Developed Countries. In developed countries, the main gap in our knowledge is for data on prepared and processed foods, especially those prepared in the home. Much of the information on these foods is derived by calculating the nutrient content from that of the ingredients and the proportion of the various ingredients given in recipes. This can give rise to errors because of the imprecision of the recipe and because of the losses and gains of individual constituents during the process. Often, for example, fat added during preparation is not eaten while the removal of water by evaporation during cooking or drainage after cooking can increase the concentration of many constituents in a food. Minor components such as water-soluble minerals, trace elements and vitamins can be discarded with the cooking water while fat-soluble vitamins can be discarded with cooking oil. Some food components, such as vitamin C are destroyed during food preparation. It is also important to know the degree of nutrification of processed foods.
Developing Countries. In developing countries, data for many nutrients and energy not only for prepared and processed foods but also for unprocessed foods are lacking. If data are available, they are often derived from data from “comparable” foods elsewhere which may not be appropriate.
Analytes of Interest
When deciding which substances to analyze in foods, priorities have to be set because analytical chemists can produce information on a very large number of food components. Thus, it depends on the nutritional problems being investigated. However, this means that a chicken-and-egg situation develops because nutritionists often do not know which food components are important if they do not have information on the concentration of the components in the food. For example with dietary fiber, it was necessary to have data on different classes of fiber before their nutritional significance could be investigated. In the past, one total value for a vitamin or a value for a particular vitamer was regarded as adequate but now, many nutritionists would like separate data on all individual vitamers. Carotenoids are an interesting case in point. In the past, only provitamin A activity was considered with 6 mg of β carotene or 12 mg of other provitamin A carotenoids being equivalent to 1 mg of retinol. However, it is now thought that carotenoids also have non-provitamin A vitaminoid activity. Thus it is possible to classify carotenoids based on the activities they possess. This is not simple because non-provitamin A vitaminoid activity is not a single function but includes a range of antioxidant activities and activity in modifying the immune response which to some extent is independent of antioxidant function (12). Individual carotenes differ in their ability to carry out the various non-provitamin A vitaminoid activities attributed to them. Thus the following classification based on that of Olson (13) uses singlet oxygen quenching activity as the non-provitamin A activity:
Type 1: Provitamin A and non-provitamin A vitaminoid activity (β-carotene)
Type 2: No provitamin A activity but non-provitamin A vitaminoid activity (canthaxanthin)
Type 3: Provitamin A activity but no non-provitamin A vitaminoid activity (β-apo-14'-carotenal)
Type 4: No provitamin A nor nonprovitamin A vitaminoid activity (phytoene)
It may well be that the non-provitamin A vitaminoid activity of carotenoids is overemphasized because measurements of carotene intake usually reflect consumption of dark green leafy vegetables and orange/yellow-colored fruits. Other minor components of such foods may have greater nutritional significance. For example, it has recently been reported that quercetin in plants is associated with lower rates of heart disease (14). Such components are generally referred to as non-nutrients, a class of substances with a wide range of structure and function. As discussed for dietary fiber, it will be difficult to set priorities in the analysis of non-nutrients because, without composition data, epidemiological studies will not be able to show whether their intake is important or not.
There are a numbers of ways which food components can be classified. Apart from classifying them as nutrients or nonnutrients, we could also classify them as favorable, neutral or unfavorable components (often depending to a large extent on the content in a particular food or the diet as a whole). However, a more useful classification may be into intrinsic substances, non-intentional food additives and intentional food additives.
Intrinsic Substances. These are absorbed from the environment or produced by the plant or animal from which the food is derived. The content of some of the components is reasonably constant while the content of other components, such as of trace elements (essential; nonessential but non-toxic; and toxic) would depend on their content in the food chain and the environment. Important intrinsic non-nutrients in foods are tannins and phytic acids which affect the bioavailability of iron (15, 16).
Non-intentional Food Additives. These are neither intrinsic to the food nor added intentionally. They include microbial metabolites, such as aflatoxin and some B vitamins, hormones, antibiotics, and components derived during storage, preparation and transport including components derived from packing materials. Thus, generally, the content of these components in foods is very variable.
Intentional Food Additives. This group comprises substances added to give the desired physical appearance or structure, organoleptic properties or nutrient value and include emulsifiers, colors, flavors and also nutrients. Generally, but not always, the content of these components is reasonably constant for a given food.
The way in which values on the concentration of components in a database are handled depends not only on the distribution or range of values encountered but also on the general usefulness of the data. If values are tightly distributed, they would be of use to a wide audience but if they are specific to the batch of food in question, they would be of use only to people with an interest in that food. Food naming and description will be very important in determining the degree to which the data can be used more generally. The extent to which the data are widely applicable will be important in determining the policy on making the data available.
Many of the basic methods for food analysis were established about one hundred years ago and there has been very little change in the principles of the methods since then, even though the apparatus used may have been automated to some extent. However for some components, the introduction of new techniques has been essential for obtaining reliable data. Such techniques include chromatography, both gas-liquid chromatography and liquid chromatography, and atomic absorption spectrophotometry, a technique which was developed in Australia. Through their use, it has been possible to generate data on the content in foods of amino acids, fatty acids and a wide range of vitamins and minerals. There are a number of tasks facing analysts today.
Development of Techniques for the Analysis of Food Components for Which No Adequate Methods Exist. Such food components include not only those which are well recognized, such as vitamin K, heme iron and non-heme iron, but also compounds which are just being recognized as having nutritional importance such as the flavonoids (17).
Development of Techniques Suitable for Use in Laboratories in Developing Countries. In western countries, equipment has become sophisticated and sometimes highly automated because of the high cost of labor and the ready availability of funds for equipment and expendables. In developing countries, often labor is relatively cheap but limited funds are available for equipment, parts and reagents. In addition, provision of constant power and water is often a problem. Since the need for data on food composition in developing countries is even more pressing than in developed countries, the development of methods suitable for use is a pressing problem. Such development will have to be accompanied by the establishment of suitable quality control procedures. Since two of the most important nutritional problems in developing countries are vitamin A and iron deficiencies, methods for the determination of provitamin A carotenoids, tannin and phytic acid should receive high priority.
Quality Control of Analyses and Determination of the Quality of Data. The use of reference materials has been discussed by Tanner et al. (18). Their proper use is essential for producing good quality analytical data. Evaluation of data is a difficult task and it needs to be made less subjective. Mangels et al. (19), have made some progress in this area by developing expert systems for the evaluation of data on the carotenoid, copper and selenium content of foods. It is essential if data in databases are going to be widely distributed that uniform criteria for data are adopted.
Levels of Data Required. When nutritionists consider food composition tables, they generally think of them for calculating nutrient intake from food intake (or vice versa) at the individual level. However, as mentioned above, data on food consumption are also collected at the household and at the national or regional level. As part of the FLAIR Eurofoods-Enfant Project, Belsten and Southgate (20) reviewed the so-called “conversion factors” for converting food disappearance data to nutrient data. Up until now, the factors are a combination of nutrient composition values with factors analogous to the extraction rate of nutrients from cereals but the system was not well documented. Thus they suggested that the factors be separated so that each component could be checked and revised if necessary. Preliminary work has also been done on food composition tables for use with household budget surveys.
• Physical Properties
Although much work has been done on the physical properties of foods, such as on viscosity, elasticity, tensile and shear strength, and water-holding properties, the information is not as readily available as that on the content of various constituents. This is an area which should receive more attention in the future. The data are not only of interest to food processors but should also be of interest to nutritionists especially those involved in bioavailability.
An area which must receive much more attention in the future is the measurement of the bioavailability of food constituents. A start has been made with a number of vitamins and minerals such as calcium, iron, zinc and a number of B vitamins but very little has been done with respect to bioavailability of other nutrients such as the carotenoids. Since bioavailability depends to a large extent on the meal in which the food constituent in question is consumed, this means that we will need information not only on daily food consumption but also on intake of other constituents at individual meals.
Recently, I have developed a series of carotene bioavailability indices (or Carbi indices) to correct carotene intake for bioavailability (West, in preparation).
Carbi-1 Index. This provides a measure of the absorption of provitamin A carotene from a given matrix relative to the absorption of the same amount of carotene dissolved in oil. Based on the work of Hume and Krebs (21), the following is a provisional list of Carbi-1 indices:
β-carotene dissolved in fats/oils, 1.00
β-carotene in cabbage and spinach, 0.53
β-carotene in carrots, boiled, sliced, 0.33
β-carotene in carrots, domestic puree, 0.33
β-carotene in carrots, homogenized, 0.73.
Carbi-2 Index. This provides a measure of host-dependent reduction in carotenoid absorption and/or conversion to retinol and would be related initially to the intake of fat (Carbi-2a index) and the degree of parasitemia (Carbi-2b index). Based on the work of Jayarajan et al. (22), the Carbi-2a index would be 0.5 when the intake of fat in children was less than 3 g/d. Similarly, based on work from our laboratory on the absorption of iodized oil (23), the Carbi-2b index in Entamoeba histolytica-infected children would be 0.25. Other Carbi-2 indices could be developed to take into account factors such as the effect of various types of dietary fibre on carotenoid absorption and of zinc deficiency on the conversion of carotenoids to retinol.
Carbi-3 Index. This provides a measure of the effect of carotene intake on the rate of conversion of β-carotene to retinol as suggested in the FAO/WHO recommendations (24). For the purposes of calculating the Carbi-3 index, carotene intake should first be corrected by applying the Carbi-1 and Carbi-2 indices.
Carbi-4 Index. This provides a measure of the extent of conversion of various carotenoids to retinol. With the Carbi-4 index for β-carotene set at 1.00, the Carbi4 index for other provitamin A carotenoids is generally set at 0.50 (24). However, the extent of conversion of these carotenoids to β-carotene varies.
The idea of such indices is not new. Monsen et al. (15) have developed a method by which the amount of iron which is bioavailable can be estimated from the intake not only of iron but also of enhancers and inhibitors of iron absorption. It is just as important for the Carbi concept to be used in order to assess whether the intake of carotene-containing foods meets the vitamin A requirements of individuals. For example, a child consuming boiled sliced carrots, with a fat intake of less than 3 g/d, and infected with Entamoeba histolytica would need to consume 24 times more of the food in order to meet requirements than the content would suggest.
• Nutritional Status
Nutritional status with respect to a particular nutrient depends to a large extent, but not entirely, on the intake of the nutrient in question. Bioavailability, concurrent ingestion of other nutrients, physiological factors, and environmental and genetic factors also play a role in determining nutritional status. Be that as it may, there is a need to examine the relationship between nutrient intake and status and to collect more information on the nutritional status of people especially at the national or regional level. Using such data in conjunction with food and nutrient intake data, it is possible to develop and monitor strategies for controlling nutritional imbalances.
• Priorities for the Future
Providing Data on Food Composition for Developing Countries
In developed countries, increased resources for generating data on food composition will require a reallocation of resources within the countries themselves (including via the Commission of the European Union). Forums such as INFOODS, FLAIR Eurofoods-Enfant and the National Nutrient Databank Conferences in the US, and meetings such as the present one will play an important role in the exchange of ideas. However in developing countries, Eastern Europe and the former countries of the Soviet Union, the needs for data are being met only poorly and the countries need assistance from outside to improve the situation. For example, in Africa, the most comprehensive source of data on the composition of foods was published by FAO in conjunction with the US Department of Health, Education and Welfare in 1968 (25). This book, as well as those prepared for a number of other world regions, is now hopelessly outdated and inadequate in terms of the number of foods, nutrients and other food components on which data are available, food naming and description, analytical methods used, and the quality control of the data. For example, many of the methods available at the time for the determination of nutrients were poorly developed. This is particularly true for the determination of provitamin A carotenoids so estimates of the amount of vitamin A which can be provided from the diet are overestimated, probably by a factor of two (26). For non-nutrients, practically no data exist. This is particularly important for those factors influencing bioavailability such as phytic acid and tannin referred to above with respect to iron.
Since 1982, a number of groups have been active in stimulating international cooperation on improving the quality and availability of data on food composition. The INFOODS project of the United Nations University has examined the needs of users (27) and developed guidelines in a number of areas such as on the description of foods (2), definition of names of nutrients with appropriate tag names which can be used when transferring data (28), and on procedures for transferring data between nutrient databases (29). In addition, they have made a start in establishing regional centers throughout the world. This effort has been strengthened by INFOODS joining forces with FAO (11). In Europe a similar organization, which has worked closely with INFOODS was established. Initially, this was referred to as Eurofoods but was later incorporated into the Food-Linked Agroindustrial Research (FLAIR) Programme of the Commission of the European Union as Eurofoods-Enfant. These organizations have been working towards the improvement of the quality and compatibility of data on food composition and consumption in Europe. There work has led to a marked improvement in the quality, comparability and accessibility of data on food composition in Europe (30). The contract supporting Eurofoods-Enfant finished at the beginning of 1994 but a new project is planned to commence at the end of 1994 through the COST mechanism of the Commission of the European Union. One activity evolving out of the Eurofoods-Enfant Project is the series of biennial Postgraduate Courses on the Production and Use of Food Composition Data in Nutrition. The Second Course, held in October 1994 under the auspices of the Graduate School VLAG (Advanced Studies in Food Technology, Agrobiotechnology, Nutrition and Health Sciences) at Wageningen Agricultural University in conjunction with UNU, FAO and the International Union of Nutritional Sciences was attended by over 30 people from about 20 countries. Such courses will help to increase expertise in the area of food composition tables and nutrient databases worldwide.
There is no doubt that in order to meet the goals of the International Conference on Nutrition, it is essential that a program of action should be instigated to produce and disseminate data on the composition of foods in developing countries and in Eastern Europe. All nutrition-related programs depend on the availability of such data in the same way as traffic depends on maps. This work will require an input of resources from industrialized countries especially the US and those in Europe.
New Developments in Computer Use
Computers are becoming faster and cheaper, data storage is also becoming cheaper and software is becoming more sophisticated. All of these developments mean that computers will be more able to serve the needs of nutritionists and food scientists. However, it is becoming more and more important to develop systems and practices to ensure the quality of both input and output from computer systems. It is all too easy to think that more is necessarily better. Users should remember the computer adage: “garbage in means garbage out”. The expert systems described by Mangels et al. (19) need to be improved and extended to nutrients other than copper, selenium and the carotenoids in order to ensure the quality of data being entered into nutrient databases is adequate for the use envisaged. Similar systems will need to be developed for dietary intake data and for ensuring the quality of food names and descriptions as well as for monitoring the quality of data output. There is a lot of pressure for online systems to be developed for supplying food composition and consumption data. In my opinion, the need for on-line systems is over-emphasized because most of the food composition data in nutrient databases in the world can be accommodated on one compact disk. Perhaps some entrepreneur would like to consider an annual version of “World Nutrient Data” on compact disk although such a venture may not be economically viable.
As far as computers are concerned, the biggest challenge is to present non-alphanumeric information. A start has been made with pictures of foods being stored on compact disk. Perhaps food texture will be recorded as the sound of a standard person biting into a standard carrot. But what about smell and taste of foods and the ethereal atmosphere in which foods are eaten? Is computer technology up to storing these data yet?
(1) McCann, A., Soergel, D., Holden, J., Pennington, J., Smith, E., & Wiley, R. (1980) Langual Vocabulary, Users' Manual, US Food and Drug Administration, Washington, DC
(2) Truswell, A.S., Bateson, D.J., Madafiglio K.C., Pennington, J.A.T., Rand, W.M., & Klensin, J.C. (1991) J. Food Comp. Anal. 4, 18–38
(3) Poortvliet, E.J., & Kohlmeier, L. (1993) Manual for Using the Eurocode 2 Food Coding System, Wageningen Agricultural University, Wageningen
(4) Pennington, J.A.T. (1995) in Quality and Accessiblity of Food-Related Data, H. Greenfield (Ed.), AOAC INTERNATIONAL, Arlington, VA, pp. 85–97
(5) FAO/WHO (1992) International Conference on Nutrition. World Declaration and Plan of Action for Nutrition, FAO, Rome
(6) Buzzard, I.M., & Willett, W.C. (Eds.) (1994) Am. J. Clin. Nutr. 59, 143S–306S
(7) Ministry of Agriculture, Fisheries and Food (1953-) Household Food Consumption and Expenditure, HMSO, London
(8) Briefel, R.R., & Sempos, C.T. (Eds.) (1992) Dietary Methodology Workshop for the Third National Health and Nutrition Examination Survey, US Government Printing Office, Washington, DC
(9) Rand, W.M., & Young V.R. (1984) Am. J. Clin. Nutr. 39, 144–151
(10) Greenfield, H., & Southgate, D.A.T. (1992) Food Composition Data: Production, Management and Use, Elsevier Applied Science, London
(11) Lupien, J. (1995) in Quality and Accessibility of Food-Related Data, H. Greenfield (Ed.), AOAC INTERNATIONAL, Arlington, VA, pp. 3–9
(12) Bendich, A. (1992) Voeding 53, 191–195
(13) Bendich, A., & Olson, J.A. (1989) FASEB J. 3, 1927–1932
(14) Hertog, M.G.L., Feskens, E.J.M., Hollman, P.C.H., Katan, M.B., & Kromhout, D. (1993) Lancet 342, 1007–1011
(15) Monsen, E.R., Hallberg, L., Layrisse, M., Hegsted, D.M., Cook, J.D. Mertz, W., & Finch, C.A. (1978) Am. J. Clin. Nutr. 31, 134–141
(16) Hallberg, L., & Rossander-Hultén, L. (1993) in Bioavailability '93: Nutritional, Chemical and Food Processing Implications of Nutrient Availability, Part 2, U. Schlemmer (Ed.), Bundesforschungsanstalt für Ernährung, Karlsruhe, pp. 23–32
(17) Hertog, M.G.L., Hollman, P.C.H., & van der Putte, B. (1993) J. Agric. Food Chem. 41, 1242–1246
(18) Tanner, J.T., Wolf, W., & Horwitz, W. (1995) in Quality and Accessibility of Food-Realted Data, H. Greenfield (Ed.), AOAC INTERNATIONAL, Arlington, VA, pp. 99–104
(19) Mangels, A.R., Holden, J.M., Beecher, G.R., Forman, M.R., & Labuza, E. (1993) J. Am. Diet. Assoc. 93, 284–296
(20) Belsten, J.L., & Southgate, D.A.T. (1992) Review of FAO Food Balance Sheet Nutritional Data, AFRC Food Research Institute, Norwich
(21) Hume, E.M., & Krebs, H.A. (1949) Vitamin A Requirement of Human Adults: an Experimental Study of Vitamin A Deprivation in Man, Medical Research Council Special Report Series No. 264, HMSO, London
(22) Jayarajan, P., Reddy, V., & Mohanram, M. (1980) Indian J. Med. Res. 71, 53–56
(23) Furnée, A.C. (1994) PhD thesis, Wageningen Agricultural University, Wageningen
(24) FAO/WHO (1988) Requirements of Vitamin A, Iron, Folate and Vitamin B12, FAO Food and Nutrition Series No. 23, FAO, Rome
(25) Wu Leung, W.T., Busson, F., & Jardin, C. (1968) Food Composition Table for Use in Africa, US Department of Health, Education and Welfare, Bethesda and FAO, Rome
(26) West, C.E., & Poortvliet, E.J. (1993) The Carotenoid Content of Foods with Special Reference to Developing Countries, USAID-VITAL, Washington, DC
(27) Rand, W.M., Pennington, J.A.T., Murphy, S.P., & Klensin, J.C. (1991) Compiling Data for Food Composition Data Bases, UNU Press, Tokyo
(28) Klensin, J.C., Feskanich, D., Lin, V., Truswell, A.S., & Southgate D.A.T. (1989) Identification of Food Components for INFOODS Data Interchange, UNU Press, Tokyo
(29) Klensin, J.C. (1992) INFOODS Food Composition Data Interchange Handbook, UNU Press, Tokyo
(30) Castenmiller, J., & West, C.E. (Eds.) (1994) Report of the Third Annual Meeting of the FLAIR Eurofoods-Enfant Project, Wageningen Agricultural University, Wageningen
Nutrition Division, National Food Administration, Uppsala, Sweden
The Opas Centre, Cambridge CB4 4WS, UK
The use of database management systems (DBMS) for handling food composition data is reviewed, together with some basic concepts underlying database design and current developments in DBMS support for food data. The results of a survey of system users in Europe, USA and Australasia indicated that facilities supporting the identification and description of foods, as well as methods for specifying compositional data, need to be extended and harmonized. Most systems are unilingual, but include synonyms for foods, while some support multiple languages. Generally, a single grouping or classification system for foods is used; it is often based on food source and built into the system of food codes. Facilities for calculating and storing measures of variation in a compositional value, and for describing the quality of a value are frequently lacking. Computer-readable composition data are usually exchanged as text files on floppy disk. Although most food information handling DBMS have been developed for the needs of a specific organization, more sophisticated software tools and international standardization (e.g. INFOODS, FLAIR Eurofoods-Enfant and multinational epidemiological studies) are encouraging collaborative development. The New Zealand Food Composition Database, the Swedish NUTSYS system and the EuroNIMS collaboration are briefly described as examples of recent developments in this area.
An increasing number of countries is compiling and publishing food composition tables. Inventories of food composition tables and nutritional software in Europe (1, 2, Slimani & Poortvliet, unpublished) showed that many organizations responsible for publishing tables use a computerized database management system (DBMS) for the handling and management of food composition data and related information. The systems were either developed in-house or based on commercial software packages, operated in various computing hardware and software environments, and were generally designed for the specific needs of the individual organization. Few of these systems were commercially available for other users. There are high costs involved in the production of high quality food composition data as well as in the development of FDBMS (Food DBMS) for handling the data. In view of this, efforts have been made, during the last decade, to improve the availability of national food composition data and to develop the means to achieve the international exchange of data. The purpose of this review is to outline modern database management techniques, including the relational model, and to give a brief overview of some existing database systems used for handling food composition data and of some recent international developments. More detailed descriptions of the handling of bibliographic information (3) and food composition information (4) using the relational model have been published.
• Databases — Basic Concepts
Although alternative data structures based on hierarchical or network models may be used in database management software, much attention is at present paid to the relational approach in the design of food information handling systems. The first of two main reasons for this is that commercially available relational DBMS (e.g. Oracle, Sybase, Ingres) provide the software of choice for many organizations. The second is that food composition data appear well suited to the application of relational principles, consisting of data values which relate to a food, a component, and to various other entities such as analytical method, analytical laboratory and literature reference. Therefore very briefly we shall review the main concepts underlying relational DBMS and note some possible limitations which might impinge on food-related information-handling facilities based on them.
Database analysis and design (5, 6) involves, inter alia, the building of a conceptual data model and its translation into a logical data model. For example an Entity-Relationship (E-R) diagram (7) may be used to model the data conceptually. This model may then be implemented using a logical data structure based on the relational model (8, 9). Some attention needs to be paid to the terminology which derives from several origins including “traditional” file processing data description, data modeling methodology such as E-R diagrams, and relational theory. Further care will be required as terminology from object-oriented data modeling becomes wide- spread. In referring to data, whether conceptual or logical, associated with basic data structures, it is convenient to use E-R terminology. Thus in the following description, “entity type” refers to a single data structure and “entity in stance” for an individual occurrence of that type of item.
All items of data in a relational DBMS are held in data structures constructed as “tables” (in the more formal literature often referred to as “relations”). Each table deals with a separate subject or entity type, e.g. a nutrient value, journal article or author. In a table each row refers to a different instance of the entity, an individual value, article or author, and each column to a particular property (or “attribute”) of the entity for which data are held, e.g. the journal, volume, issue and pagination data for a journal article. Rows are frequently referred to as records and columns as fields.
It is a requirement of the relational model that each row of the table is uniquely identified by the data for one of its attributes (or the combined data of more than one attribute); the identifier is known as the table's “primary key”. A primary key may include meaningful data, e.g. food name (or food group) for a food item, or have no meaningful content, being for example a sequentially assigned number. Care must be taken in selecting a meaningful key since it must remain unique over any valid items which may need to be added to the table and must remain constant for the given item; so-called “intelligent keys” are usually avoided. Any identifier whose assignment rules, assignment or use are external to the system under design should be treated with similar caution. For example, neither Chemical Abstracts Registry Numbers nor ISBN (for example as for the 4th Edition of The Composition of Foods (10) are unique for substances or books, respectively, at the level appropriate to an FDBMS. Equally for meaningless keys, adequate checks must be made whether incoming data belong to a new row (a new entity instance), or update an existing row. Although this may not be a significant problem for real-life discrete objects and events such as employees and sales, less discretely defined entity instances, as in food items, may provide some difficulty.
The “relationships” of an E-R model are associations between instances of one or more entity types, the “degree” of the relationship being the number of entity types involved. A “unary” or “recursive” relationship is a link from one instance to another of the same entity type. A common example is an entity type in which instances have a hierarchical organization, as with employees in a management structure or in a facet of a food description language. Usually a relationship is between two (binary), three (ternary), or occasionally more entity types. A further property of a relationship is its “cardinality” which expresses the number of instances which can partake from each side of the bidirectional relationship. Cardinalities are often expressed as 1:1 (a “one-to-one” relationship), 1:N (a “one-to-many” relationship) or M:N (a “many-to-many” relationship). However further detail is important, in particular whether the cardinality is mandatory or optional, i.e. whether for a one or many cardinality the minimum requirement is one or zero occurrences. A maximum or minimum number of occurrences may apply to “many” cardinality; this is less significant for data structure but important for data validation.
The relational data model provides the means to eliminate the redundant storage of information in the database which would result in possible inconsistencies during data insertion, deletion and amendment procedures. In the process of “normalization” the structures of tables are subjected to a series of steps in which dependent attributes are removed to separate tables. For example, a table of food component values may include attributes concerning the analytical laboratory. However these details should not be repeated in each row corresponding to a value generated by that laboratory. Instead details of the laboratory such as its name, address and contact person are removed to a separate table linked by a one-to-many laboratory-to-values relationship.
Table I. Component value table
The relationship links between tables are made through a “foreign key”, an attribute which records the primary key for a row in (except for a unary relationship) another table thus pointing to related data. A direct link can be made between tables for 1:1 relationships and for 1:N relationships, since more than one table row on the N side of the relationship can hold the same foreign key. Since this can only apply in one direction, an M:N relationship is stored by breaking it into two 1:N links through the insertion of an additional table. This has an attribute column for the primary key of each entity table of the relationship. Although these keys can be repeated as necessary, any one row of the relation table has a unique combination of the keys from the entity tables, and indeed the combination serves as its primary key. In practice, data can be associated with the M:N relationship and stored as attributes in the table. For example the relationship between authors and published papers is M:N, with details on each held in separate tables. However data concerning the authorship of a given paper must be held in the linking table. In particular, the position of a given author must be recorded here if the ordered list of authors needs to be reconstructed, for example in the formatting of references.
A central component of a food composition database, the component value record, can be considered further. A table for such data must include foreign keys at least to identify the food and the component. It will also have actual data such as the value, its units, and perhaps reports of quality, precision, status, etc. Values for the same food-component pairing will need to be differentiated through additional entity types such as literature reference or analytical detail which may include identification of the laboratory. To avoid having separate attributes pointing to literature and analytical source information, a separate attribute could be defined for “source type” to point to different tables (e.g. Literature or Analytical). This is then required in the unique key since duplicate values of the single attribute (SOURCE_ID) might appear in both the literature and analytical information tables. Thus, with the columns constituting the unique key delineated in bold, the component value table might be constructed as in Table I.
In Table I, components are identified using INFOODS tagnames (11, 12). The first three represent β-carotene equivalents, retinol and vitamin A (as retinol equivalents). A separate tagname, VITAA, identifies vitamin A determined by bioassay and another, VITA, indicates a value for vitamin A whose method of determination is unknown. The attribute UNIT_ID identifies the unit in which the value is expressed. Those in parentheses are default values for the corresponding component. This information could be held for the component and might be omitted in this column. Note the default unit for VITAA is IU. However it is μg/100 g for VITA- and thus the unit of IU must be held explicitly for the final row.
An important aid to the standardization of development in relational database applications has been Structured Query Language (SQL; often pronounced “Sequel”) which is based on work done by IBM in the 1970s. SQL provides a concise set of commands to support the definition, display and updating of relational tables (a broad interpretation of “query”!). These facilities include the handling of “views” providing in a convenient derived table the data based on a subset of rows and columns selected from underlying existing tables. Views are dynamic; changing their data changes the corresponded stored data and any change in the underlying data is reflected in the values displayed in a view.
Basing the design of an FDBMS on the relational model allows similar data structures and data management procedures, using SQL, to be implemented in a wide range of hardware and software environments. However various shortcomings in the use of the relational model for FDBMS have been noted (13) and a more extensive review, particularly with respect to the limitations of SQL for the management of statistical data, has been published (14). The relational model needs each instance of an entity type to be clearly distinguishable from all other possible instances. This is often not the case with entity types required in an FDBMS such as food components, analytical methods and particularly, as we note below, food items. Also relational systems may not be adept at handling the textual information needed to document such entities and the statistical descriptions associated with component values.
A fundamental problem for food information management is the underlying assumption, apparently required if the relational model is to be applied, that a food item must be uniquely defined and distinguished from all other food items. This may provide difficulties, inter alia, with variants of composite foods and when switching contexts, for example between composition and consumption records and between nutrient and non-nutrient studies. A possible alternative staying within the relational model is to avoid predefining items in the data collection at the level normally considered an individual food item. Instead every distinguishable instance or sample could be stored as separate records, with data being aggregated on search criteria (given that an adequate system of food indexing is available) or through links between equivalent items as identified by data managers and stored in a food correlation table. The latter approach would allow separate sets of aggregate items to be maintained, for example to support food table production and non-nutrient work.
Further circumstances where the “all or nothing” separation of food items is unsatisfactory include seasonal data sets which vary in only one or two components and the need to apply taxonomic and alternative names to each item derived from one raw food. Although relational solutions can be envisaged, new developments, in particular an object-oriented model, may provide an approach which more closely represents the real world. In object-oriented programming a key concept is the “class” object which can hold a number of objects of different types (such as various types of variable, data structure and function), together with functions for manipulating the objects and mechanisms to control inheritance and access from other classes. Variables and functions declared for the class are known as class members. A derived class can be declared, inheriting members from one or more base (parent) classes. In the derived class, new members may be declared and existing ones redefined. It may be that an object-oriented data model will handle the characteristics acquired (for example, in processing or cooking) by a derived food item more effectively, including the multiple inheritance from the various ingredients implicit in composite foods.
• Facilities of Existing Food DBMS
In order to obtain additional information on facilities available in DBMS implemented for handling food composition data, a questionnaire was sent to a selection of 20 system users in Europe, USA, Australia, New Zealand and the Pacific. The questionnaire focused on aspects of information handling of foods and components, recipe calculation, and storage of compositional data. The system users were also asked to give examples of improvements they would consider to be most important. The purpose of the survey was to compare how systems differed in the handling of food and component information and thus to identify areas that could be considered problematic in relation to international exchange of food information.
Table II. Food information technology
|Food||A general term, sometimes used more specifically for a basic (e.g. unprocessed) food item.|
|Food item||A specific term for a unique entity which can be differentiated from all other food entities with which it may be compared.|
|Food identification||The decision whether two food entities can be considered the same food item. This may be achievable within defined objectives or particular data collections, but such decisions may not be valid in any broader context, e.g. data exchange.|
|Food identifier||Any tag (code, name, etc.) which is unambiguosly associated with (but not necessarily unique for) a food item.|
|Food code||A code (which may be a sequential number based on an ordered list of items or incorporate some degree of hierarchical classification) used to identify a food item unambiguously.|
|Food name||A name assigned to a food item considered sufficient to distinguish it from all other items which may also occur in the data collection. The names for existing items may need to be made more specific when new, similar items are added.|
|Food description||Information on a food item which may be relevant to the data (e.g. on composition) associated with it; the information may reside in the food name, in an overlying classification or as additional descriptive detail.|
|Food descriptor||A terms included in a more or less formal set used as a food description system. The system may be faceted where the descriptors are organized in subsets according to the attribute described, e.g. preservation method, cooking method.|
|Food grouping||A categorization of food items based on an individual attribute or a selection of attributes which groups the items usefully within a given (broad or narrow) context.|
|Food classification||Any grouping system for food items (often using hierarchical categories) which attempts to assign a single “correct” (i.e. unique) locationa for any food item.|
|The primary food classification, based on a single hierarchy, which a food information system uses; generally based on food type and/or source.|
|Parallel _ _ _ systems||Where more than one independent system of either food codes, names, description, grouping or classification coexist in an implementation, these are referred to as parallel systems.|
a If unique locations are defined down to the level of each individual item, the location may also be considered unambiguous with respect to that item. However this approach to food identification will only be canonical if incontrovertible rules can be defined for assigning items to locations. This is highly improbable for foods.
The organizations covered were mainly those responsible for official national food composition data compilations but others mainly involved in dietary surveys and epidemiological research were also included. Answere were received for 17 systems. Fifteen systems were developed in-house using various programming languages or commercial DBMS (e.g. Oracle, Advanced Revelation, dBASE III). No clear preferences were evident for the hardware environment, programming language or DBMS; it appeared these were determined by practise and availability within the organization.
Information Handling of Foods
Food databases contain records on food items and the information detailing these is crucial. Thus the identification, description, grouping and classification of foods, the ways of representing information on the items, are key areas in the handling of food information. However, there are still no universally accepted definitions and taxonomy of these terms, which would be desirable when using them in an international context. Table II shows suggested definitions for a number of terms used in the paper.
According to the questionnaire responses, foods are usually identified by their names and a code. Some systems (one-third of those used for data compilation but none used for dietary survey work) can include several coding systems in parallel. Two systems use Langual (15), and six a less formal but similar type of descriptor, for a more detailed food description. Otherwise the name is used for describing the food, sometimes supplemented with additional free-text description. Most systems can include synonyms for foods but are unilingual, although six systems allow food and component names to be held in multiple languages.
Generally, a single grouping or classification system for foods is used; it is often based on food source and may be built into the coding system. Half of the FDBMS used for national food composition data allow the use of multiple grouping or classification systems in parallel.
Preferably, a FDBMS should allow the use of multiple coding systems, e.g. parallel management of national food codes and Eurocode 2 (16), and of parallel grouping and classification systems based on different criteria. For the use of international data it is also necessary to be able to handle in parallel multiple food names, including different language versions.
Information Handling of Recipes
A recipe system for calculating the composition of dishes or mixed foods from their ingredients is usually needed in a FDBMS. Apart from calculating the composition of cooked dishes, such a system can be used to estimate the composition of mixed foods from the proportions and compositions of their constituents. A recipe system is included in fourteen of the systems reviewed, especially in those mainly intended for processing of data from dietary surveys. About half of the recipe systems allow for the use of alternative preparation methods, portion sizes, and yield factors for change in weight/water content during preparation but facilities for adjustments in fat content were included in five systems. Ingredient substitution (e.g. between different types of fats) in recipes is possible in some systems. The application of retention factors for components (e.g. vitamins) has been included in the calculations with varying degrees of sophistication in eight of the systems. Although there are many difficulties in accounting for losses and gains during food preparation, facilities to allow for these are in many situations essential.
The ability to break down recipes by ingredient and to calculate the contribution of each ingredient to each component was possible in five systems. Such a function is of importance, e.g. when calculating the contribution of various foods or food groups to various nutrients. Flexibility in input of recipe data is desirable and ideally it should be easy to enter recipes expressed in both household measures and grams. It should also be possible to modify a recipe, e.g. exchange alternative liquids, fats, etc.
Information Handling of Component Data
Various considerations are important in the storage and handling of component data, especially in an international context. These can be subdivided into two main areas, identification of the component reported and the details of the compositional value stored.
In the systems reviewed, components are usually identified by a code and/or by a name or abbreviation. Two systems also use the INFOODS tag system (11, 12) in parallel. The INFOODS tag system was developed to uniquely identify components, especially in data exchange.
Several systems have separate databases for the compilation of “raw” data (the “working” database) and for the “official” values used for publication of food tables, calculation of intakes from dietary surveys, etc. Component data are stored as single analytical results for individual samples (in the working database) or as mean values derived from analytical data or data from other sources. There was generally a lack of facilities for calculating and storing measures of variation in a value as well as for reporting the assessed quality of a value. Component values from other organizations, together with a reference to their source, could be included, but generally the imported values were not stored in the original data format (thus, for example, not necessarily retaining the original precision). Indication of the period for which an official value is or was valid, possible in four systems, is a useful facility, e.g. for reconstructing previous databases used for dietary surveys or earlier editions of food tables.
The compositional data for a given component in a given food can be stored in various forms, ranging from individual results for each individual analytical portion to only a single derived or imputed value. Ideally, a database would contain mainly analytical data based on verified methods. Food analysis is, however, costly and requires large resources. Therefore many organizations also use data from the literature and also alternative methods for calculating or estimating component levels. A comprehensive FDBMS should support facilities for recording literature references and also the means to indicate the quality and method of derivation of a value.
Half of the organizations use their FDBMS for the production of food composition tables. Three systems directly output data formatted for publication of food tables, while six use commercial word-processing software such as WordPerfect or Microsoft Word for editing the data. The underlying principle in using a relational DBMS is that a given item of data is only entered and stored once, but repeated as necessary on output, for example when a food name appears in the main food table and in the food index.
Other Aspects of Information Handling
Exchange of computer-readable composition data was common, with text files on floppy disk being the most widely used format.
User friendliness is important for any software handling complex information like food composition data, a graphical user interface (GUI) being preferable. This should provide the ability to interchange data with commercial software, e.g. spread-sheets, word processing, statistical packages, since each of these support specialist facilities which it is not practical to implement in a DBMS. Another aspect of user friendliness is the inclusion of individual profiles for users so that their working environment is customized when they log on, e.g. by setting their preferred working language, code system.
The systems users were asked to state the three improvements that they would consider the most important if they were about to enhance their system. Responses included improved facilities for the calculation and storage of compositional data (including measures of data quality), for recipe calculation and the handling of multi-constituent foods, and for food classification and aggregation. Better user-friendliness in general was cited, as well as specifically a GUI, and there was a requirement for multilingual support, particularly of food names. A need for greater flexibility was expressed, for example in allowing extra components to be included, the modification of existing recipes and the handling of user-specific data (e.g. “own foods”). In general the results seem to imply a considerable agreement on the overall facilities which are required in a comprehensive system when the resources are available to implement them.
• Current Developments in International Food Databases
In addition to supporting the facilities required for the handling of food information, a modern FDBMS should be based on modern computing and informatics techniques and standards. This should allow a flexible design which made the system easy to enhance and modify. The interface should be user friendly and preferably be a GUI. The design and operating environment should allow for data exchange, both with other applications such as spread-sheets and with other FDBMS.
Currently the computing techniques most appropriate to FDBMS involve relational databases accessed through SQL, although as noted earlier these may not prove a perfect solution and potentially better alternatives may become available. They do, however, provide a basic standard, making practical collaborative developments to create transportable systems implementable in the current hardware and software environments of many organizations. Such developments should also encourage the implementation of compatible data structures and the application of standard policies to the food-related data stored, key aspects in improving the effectiveness of data exchange.
Until recently, most FDBMS were developed specially for the needs of an individual organization, in part because the development and use of common software had been limited by compatibility and portability problems. Generally the systems have not been available for purchase on a commercial basis. However there are high costs involved in production of high quality food composition data as well as development of DBMS for handling the data. The sharing of development costs to produce a highly functional system would enable the most effective use to be made of the analytical data obtained. Increased standardization, more sophisticated software tools, and international cooperation (e.g. INFOODS, Eurofoods-Enfant and multinational dietary and epidemiological research) have stimulated interest in a DBMS capable of handling high quality food data, which would allow the use of multiple languages, coding, description and classification systems for foods and components (17–19).
Recent FDBMS developments include the New Zealand Food Composition Database, the Swedish NUTSYS system, and the EuroNIMS collaboration.
New Zealand Food Composition Database
The New Zealand Food Composition Database is designed to handle data from different countries in a flexible way (20). It has been developed in-house using Advanced Revelation DBMS and its programming language Rbasic and is operated on a PC network. It is well suited for easy data interchange with other countries and institutions, while maintaining the ease of information and data output, in both electronic and hardcopy formats. In addition to using various facets for describing and naming foods, the system includes images of foods (color photographs), which are linked to the compositional data. The system is now installed in Latin America (INCAP in Guatemala), the South Pacific Commission (New Caledonia) and for ASEANFOODS (at INMU in Thailand).
NUTSYS is the name of a prototype FDBMS developed at the Swedish National Food Administration (21). It is the result of a project to develop a modern, flexible DBMS for handling food composition data. A number of functions and modules were identified by a project group that ideally should be included in a fully developed system. Some of the most important were:
registers for foods, nutrients and other components
database for nutrients and other components
recipe calculation system
system for compositional data source references
modules for print-out of food composition tables
system for handling data from dietary surveys
system for menu planning.
The system was designed to contain functions that allow for:
storage of an “unlimited” number of foods, recipes, components
indication of the origin, quality, source, etc. of a value
indication of the period during which a value is valid
indication of the origin (country, region) of a food
indication of the method of preparation and processing of a food
indication of the density, portion weight, etc. of a food
grouping of foods and components according to different criteria
use of different names, synonyms, languages, codes, measures, etc.
breakdown of recipes to ingredients and exchange of recipe ingredients
use of yield and retention factors in recipe calculation
easy communication with other systems.
A data model was outlined with a number of entities and concepts. Based on the model, a prototype was constructed using the Ingres 4GL tool Vision. About 70 programs were generated and completed.
Development of a new system (EuroNIMS, European Nutrition Information Management System) began after the start of the NUTSYS project. The EuroNIMS cooperation is a result of an initiative from the Belgian NUBEL Foundation, responsible for the management of national food composition data, and NIMS representatives. NIMS (NUBEL Information Management System) is a software package for management of nutrient composition data currently being used by the NUBEL Foundation in Belgium and was developed by Logimed, a software development company. NIMS supports some of the key functions defined in NUTSYS, e.g. multiple languages, coding and classification systems for foods. Representatives from about a dozen European countries and one international organization (IARC) have participated in the discussions on EuroNIMS.
The design of EuroNIMS is based on a client-server software architecture in which networked PCs or workstations, as “clients”, access data held on a central machine, the “server” (although in practice a single, powerful PC could support both the client and server functions). The database is held on the server using a proprietary DBMS such as Ingres or Oracle, perhaps one already installed by the user. EuroNIMS interacts with the DBMS through an ODBC (Microsoft Open Database Connectivity) interface and this uses a single dialect of SQL. At the client end, data exchanged with the server will be processed through an object-oriented DBMS to be presented to the user with a graphical user interface (GUI). In the first EuroNIMS software release (Version 1.0), client machines use Windows 3.X as the GUI and the server runs under Windows NT. As a result, EuroNIMS uses 16-bit Unicode data storage, but with 8-bit images in parallel to accommodate operating environments using current character storage conventions.
EuroNIMS Version 1.0 includes most functions defined in NUTSYS. Features of particular interest include:
multilinguality both at the user interface and data storage levels
international food identification (country, organization, sequential and version number)
parallel management of different coding and classification systems
registration of food manufacturers and distributors and of analytical laboratories
registration of items as aggregated or representative foods
a range of algorithms for the calculation and conversion of values
recipe storage with link to spreadsheet calculation using yield and retention factors
facilities for Langual encoding.
The use of up-to-date computing techniques allows FDBMS currently under development to support more comprehensive facilities than hitherto, for example in the handling of documentary information and images, accessed through user-friendly interfaces. In addition to providing an effective operational environment for the compilation of food composition and related data, the systems are increasingly being developed and implemented on the basis of international cooperation. This complements the efforts of the past decade in establishing guidelines for such data collections and should prove to be an important step in facilitating the use and exchange of high quality food composition data.
The authors thank the following for kind assistance in completing the DBMS questionnaire and supplying further information: D. Buss and M. Day, UK; M. Buzzard, USA; F. Cook, New Zealand; K. Day, UK; M. Hoke, USA; D. Douglass, USA; J. Ireland-Ripert, France; J. Klensin, INFOODS; J. Lewis, Australia; B. O'Shea, Ireland; J. Taylor, UK; A. Trichopoulou, Greece; A. Turrini, Italy; L. Valsta, Finland; A. Walker, UK; C. E. West, The Netherlands.
(1) West, C.E. (Ed.) (1989) Inventory of European Food Composition Tables and Nutrient Database Systems, National Food Administration, Uppsala
(2) Loughridge, J.M., Walker, A.D., & Towler, G. (1993) Inventory of Nutritional Software, FLAIR Eurofoods-Enfant, Wageningen Agricultural University, Wageningen
(3) Crawford, R.G. (1981) J. Am. Soc. Inf. Sci. 32, 51–64
(4) Feinberg, M., Ireland-Ripert, J., & Favier, J-C. (1992) World Rev. Nutr. Diet. 68, 49–93.
(5) McFadden, F.R., & Hoffer, J.A. (1994) Modern Database Management, 4th Ed., Benjamin-Cummings, Redwood City, CA
(6) Jennings, R. (1993) Using Access 1.1 for Windows, Special Ed., Que Corporation, Carmel, IN
(7) Chen, P.P-S. (1976) ACM Trans. Database Syst. 1, 9–36.
(8) Codd, E.F. (1970) Comm. ACM 13, No. 6
(9) Date, C.J. (1981) Introduction to Database Systems, 3rd Ed., Addison-Wesley, Reading, MA
(10) Paul, A.A. & Southgate, D.A.T. (1978) McCance and Widdowson's The Composition of Foods, 4th Ed., HMSO, London
(11) Klensin J.C., Feskanich, D., Lin, V., Truswell, A.S., & Southgate, D.A.T. (1989) Identification of Food Components for INFOODS Data Interchange, UNU Press, Tokyo
(12) Klensin J.C. (1992) INFOODS Food Composition Data Interchange Handbook, UNU Press, Tokyo
(13) Klensin J.C. (1991) Trends Food Sci. Technol. 2, 279–282
(14) Klensin J.C., & Romberg, R.M. (1989) Lect. Notes Comput. Sci. 339, 19–38
(15) Hendricks, T.C. (1992) World Rev. Nutr. Diet. 68, 94–103
(16) Poortvliet, E.J., Klensin J.C., & Kohlmeier, L. (1992) Eur. J. Clin. Nutr. 46 (Suppl. 5), S9–S24
(17) Truswell, A.S., Bateson, D.J., Madafiglio, K.C., Pennington, J.A.T., Rand, W.M., & Klensin J.C. (1991) J. Food Comp. Anal. 4, 18– 39
(18) Greenfield, H., & Southgate, D.A.T. (1992) Food Composition Data: Production, Management and Use, Elsevier Applied Science, London
(19) Simopoulos, A.P., & Butrum, R.R. (1992). World Rev. Nutr. Diet. 68, 1–160
(20) Cook, F., Duxfield, G., & Burlingame, B. (1992) Proc. Nutr. Soc. NZ 17, 204–207
(21) Becker, W. (1993) NUTSYS — a Food and Nutrition Composition and Information Management System, National Food Administration, Uppsala