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DAPA Measurement Toolkit


Food composition data and databases


Food composition databases (FCDBs; also known as food composition tables) are made up of food composition data (FCD) that are detailed sets of information on the components of individual foods. As such they include information on values for energy, macronutrients, and micronutrient vitamins and minerals, and food components such as fibre.

FCD are important for dietary research as well as for use in nutrition policy, clinical and public health practice, and by the food manufacturing industry. They are used for national programmes for the assessment of diet and nutritional status at a population level, and also in nutrition labelling.

Different countries have FCDBs that are appropriate and relevant to the foods consumed in that setting.

For instance, in the UK, the commonly used FCDB is the McCance and Widdowson’s “The Composition of Foods”. Since 1940 this has been updated several times, and the latest in the update is the 7th Summary Edition [4]. This version provides comprehensive data on all food groups, and includes nutrient data for over 1,200 of the most commonly consumed foods in the UK. Also available as McCance and Widdowson’s 'composition of foods integrated dataset' (CoFID) online via the UK Government webite.

In the USA, the US Department of Agriculture (USDA) provides FCDBs relevant to American diets. Similarly, other countries have their own FCDBs, with key information about them often available via the internet.

Foods from these national FCDBs can now be identified by International Food Code (IFC).

In nutritional research, FCDBs are critical to convert reported food intakes into energy and nutrients. It is important to understand their potential limitations.

The potential sources of error in food composition values may be random or systematic, and all such errors add additional uncertainty to the calculated nutrient intakes [5]. Limitations in the use of food composition databases can involve the following seven domains:

1. Bioavailability

Food composition values represent the total amount of the constituent in the food, rather than the amount actually absorbed in the body. As a consequence, the potential bioavailability of nutrients in the local diet should be considered when nutrient intake data are assessed.

2. Natural variability in the composition of foods

Foods exhibit natural variation in the amount of nutrients they contain The level of certain nutrients in some foodstuffs will not differ between countries, but for other nutrients, factors such as those listed below may affect nutrient content [6]:

  • Plant husbandry
  • Soil
  • Weather
  • Transport
  • Storage conditions

In addition, recipes for the same composite dish may vary both within and between countries, as might fortification practices (e.g. the fortification of flour and breakfast cereals). For example, in contrast to many European countries, the selenium content of foods is higher in the United States and Canada due to the higher selenium levels in the soil, while levels are particularly low in New Zealand. Ideally, each country should compile its own food composition database.

3. Natural variation in nutrient contents also exists within food types:

Meat products

The nutrient composition of meat products can vary greatly depending on the proportion of lean to fat tissue. The ratio between the two also affects levels of most other nutrients.

Fruits and vegetables

Storage conditions can affect the water content of plant foods. Changes in water content are associated with changes in all other constituents mainly as a result of changes in nutrient density. Trace elements are affected by husbandry conditions, soil composition and fertiliser use.

Cereal products

The natural variation in nutrient content of cereal products (e.g. flours and grains) is less than that in fruits and vegetables. However, as with other foods, fertiliser and soil type produce some variation in mineral content. Different fortification practices in some countries markedly affect micronutrient levels such as B vitamins, folate, iron and calcium. For example:

  • In the US mandatory fortification of grain with folic acid was introduced in 1998
  • In Canada, mandatory fortification of white flour and pasta has been carried out since 1998

4. Processed foods and composite dishes

The nutritional content of processed foods and composite dishes varies greatly. Processed foods change in formulation and production and the introduction of reduced-energy, saturated fat and salt versions of standard foods mean that databases need to be continuously updated. Common foods such as mayonnaise, muesli and sausages vary by brand and many supermarkets have introduced their own brands with different formulations and fortification levels. Composite dishes show great variation mostly due to differences in recipes and actual cooking method.

5. Coverage of food items and nutrients

The number of dietary items (e.g. foods, meals, and supplements) and nutrients or other food chemicals can vary widely by database.

Dietary items include:

  • Single food items
  • Meals
  • Manufactured products with different brands/retailers
  • Dietary supplements with different brands
  • A single food item by a production system (e.g. grass-fed beef, grain-fed beef)

Entry values include:

  • Energy and standard nutrients (e.g. protein, vitamins and minerals)
  • Trace metals (e.g. mercury, cadmium)
  • Physiological measures (e.g. glycemic index, a measure of glucose elevation after consumption, by the University of Sydney)
  • Phytochemicals (e.g. plant sterol)
  • Food additives, pesticides, pollutants
  • Economic measures (e.g. price)

It is unlikely that a database can be comprehensive for more than a short period. New foods are constantly introduced to the market and although modern databases can hold information on a large number of different foods, only a limited number of foods can be practically included in a database.

Ideally, food composition databases should include complete data on all nutrients known or thought to be important to human nutrition. However, this can rarely be achieved. Factors such as the availability of reliable analytical methods, the availability of existing data, health concerns in a given country (and hence priorities given to certain nutrients) as well as national and international labelling regulations are all determinants of the coverage of nutrients in a database.

6. Labelling

Nutrition values declared on food labels include a tolerable margin, and as such, using values obtained from food packaging may add a degree of error to the food composition databases. Furthermore, some countries have different guidelines. In the UK the acceptable level of tolerance permitted for macronutrients decreases as the level of the macronutrient in a product increases (see Table D.1.12).

Table D.1.12 Food labelling tolerance levels for protein, fat, carbohydrates and dietary fibre.
Declared values Recommended tolerance
More than 5% ± 20% of declared value
Between 2% and 5%/td> ± 30% declared value
Less than 2% Use discretion based on the specific individual circumstances
Note 1: For those values above 5% seasonal/natural variability should be considered for meat.
Note 2: For wholemeal cereal products and saturated fats, higher tolerances may apply.

7. Analytical variation

A number of variations in terminology and analysis between countries can result in different values for nutrients for the same food and hence total intakes. The most notable of these are carbohydrate and dietary fibre.


In many countries, including the US and most countries in Europe, carbohydrate is determined ‘by difference’ as the remainder after subtraction of the values for protein, fat, water and ash from the weight of a food.

In other countries, such as the UK, carbohydrate is the sum of the components of carbohydrate subtypes, namely starch and sugars, added together.

These two approaches give different values for many foods particularly those containing dietary fibre and more complex types of starch. Moreover, the energy values applied to carbohydrate also varies, in most countries being a value of 4 kcal/g, whereas in the UK the value applied is the monosaccharide equivalent of 3.75 kcal/g. Hence the same value of the carbohydrate content of a food will have a different energy value, and diets will have different energy values from countries using one method versus the other. These differences are often not appreciated in international comparisons.

Dietary fibre

Fibre content is analysed in most countries by the Association of Official Analytical Chemists (AOAC) [8, 12] to give a value for total dietary fibre. There are different AOAC methods available; the older methods (AOAC 985.29 and 991.43) capture non-starch polysaccharides, some resistant starches, lignin and some inulin, but they do not measure most non-digestible oligosaccharides [9, 13]. A newer method of analysis is now available (AOAC 2009.01) which is able to determine more components [10, 11].

The Scientific Advisory Committee on Nutrition (SACN) recommends a new definition of dietary fibre be adopted in the UK where dietary fibre should be defined as all carbohydrates that are neither digested nor absorbed in the small intestine and have a degree of polymerisation of three or more monomeric units, plus lignin [15]. Since 2015, it is recommended that the dietary reference value for the average population intake of dietary fibre for adults should be 30g/day, contrary to the previous dietary reference value of 18g/day of non-starch polysaccharides defined by the Englyst method, which is equivalent to about 23-24 g/day. The SACN recommendations are measured using the AOAC methods. In some countries the method of Englyst et al is used to measure dietary fibre [3].

These give different values for most foods, those for total dietary fibre being generally higher than non-starch polysaccharide. The fibre method used should always be specified in publications reporting dietary intakes.

Although national food composition databases are needed, international studies highlight the need for standardising food composition data produced at the national level [6]. For example, researchers carrying out the INTERMAP study updated existing nutrient databases from each of four countries. Information on new foods were added, and, as far as possible, the analytical methods used to estimate composition data were standardised to increase the comparability between countries in the analyses of nutrient intake [14]. Other research groups have also explored the need to revise nutrient databases to accurately estimate nutrient intake in dietary assessments [7].

In recognition of the need to increase comparability in food composition information from different countries, a number of collaborations and networks have been set up. Examples include

  • Accurate and reliable estimation of nutritional intake is key to nutritional epidemiology. Food composition data provide an approximation of the energy and nutrient composition of foods. Calculating nutrient intake involves matching as closely as possible the foods and drinks recorded in a diet record or food frequency questionnaire (FFQ) with the items included in the database of the dietary analysis programme. This can be a time consuming and lengthy process and is not always achievable without some error.
  • Although modern databases contain information on a large number of foods it is not possible to maintain a database that incorporates all the different foods consumed by a population.
  • Vast changes in the food supply add to the difficulties encountered with estimating calculated nutrient intakes. The range of ‘ready meals’ has increased and new foods such as low fat, and reduced sugar items have entered the market place as have consumables fortified with additional nutrients.
  • Data entry programs that allow new foods and portion sizes to be added to the existing database are essential if research into diet-disease relationships is to be carried out accurately and reliably. Figure D.1.12 highlights some of the factors influencing the accuracy of nutrient intake estimation.
  • Even though nutrition analysis software is designed to reduce reliance on nutritional knowledge and efforts are taken to standardize coding, skilled diet coders will still be required to make judgement decisions when coding diet records.
  • Moreover, although values in food composition databases can be updated, it should be remembered that food composition data are, by their very nature, quite variable.
Figure D.1.12 Factors influencing the accuracy of nutrient intake estimation.
Adapted from: [5].


  1. Braakhuis AJ, Meredith K, Cox GR, Hopkins WG, Burke LM. Variability in estimation of self-reported dietary intake data from elite athletes resulting from coding by different sports dietitians. Int J Sport Nutr Exerc Metab. 2003;13(2):152-65.
  2. Conway R, Robertson C, Dennis B, Stamler J, Elliott P. Standardised coding of diet records: experiences from INTERMAP UK. Br J Nutr. 2004;91(5):765-71.
  3. Englyst HN, Quigley ME, Hudson GJ. Definition and measurement of dietary fibre. Eur J Clin Nutr. 1995;49 Suppl 3:S48-62.
  4. Food Standards Agency. McCance and Widdowson's the Composition of Foods. Seventh Summary ed. Cambridge: Royal Society of Chemistry; 2014.
  5. Gibson RS. Laboratory assessment. Principles of Nutritional Assessment 2ed. Oxford: Oxford University Press; 2005. p. 373-402.
  6. Greenfield H, Southgate DAT. Food Composition Data: Production, Management and Use (2nd edn.). London Elsevier; 2003.
  7. Ishihara J, Inoue M, Kobayashi M, Tanaka S, Yamamoto S, Iso H, et al. Impact of the revision of a nutrient database on the validity of a self-administered food frequency questionnaire (FFQ). J Epidemiol. 2006;16(3):107-16.
  8. Joint FAO/WHO Expert Consultation. Carbohydrates in Human Nutrition: Food and Agriculture Organization of the United Nations; 1997 [30th November 2016]. Available from:
  9. Lee SC, Prosky L, De Vries JW. Determination of total, soluble, and insoluble dietary fiber in foods: enzymatic-gravimetric method, MES-TRIS buffer: collaborative study. J AOAC Int. 1992;75:395-416.
  10. McCleary BV, DeVries JW, Rader JI, Cohen G, Prosky L, Mugford DC, et al. Determination of total dietary fiber (CODEX definition) by enzymatic-gravimetric method and liquid chromatography: collaborative study. J AOAC Int. 2010;93(1):221-33.
  11. McCleary BV, DeVries JW, Rader JI, Cohen G, Prosky L, Mugford DC, et al. Determination of insoluble, soluble, and total dietary fiber (CODEX definition) by enzymatic-gravimetric method and liquid chromatography: collaborative study. J AOAC Int. 2012;95(3):824-44.
  12. Prosky L. What is dietary fiber? J AOAC Int. 2000;83(4):985-7.
  13. Prosky L, Asp NG, Schweizer TF, DeVries JW, Furda I. Determination of insoluble, soluble, and total dietary fiber in foods and food products: interlaboratory study. J Assoc Off Anal Chem. 1988;71(5):1017-23.
  14. Schakel SF, Dennis BH, Wold AC, Conway R, Zhao L, Okuda N, et al. Enhancing data on nutrient composition of foods eaten by participants in the INTERMAP study in China, Japan, the United Kingdom, and the United States. ‎J Food Comp Anal 2003;16(3):395-408
  15. Scientific Advisory Committee on Nutrition (SACN). Carbohydrates and Health report. London, 2015. Available from:
  16. Welch AA, Luben R, Khaw KT, Bingham SA. The CAFE computer program for nutritional analysis of the EPIC-Norfolk food frequency questionnaire and identification of extreme nutrient values. J Hum Nutr Diet. 2005;18(2):99-116.
  17. Welch AA, McTaggart A, Mulligan AA, Luben R, Walker N, Khaw KT, et al. DINER (Data Into Nutrients for Epidemiological Research) - a new data-entry program for nutritional analysis in the EPIC-Norfolk cohort and the 7-day diary method. Public Health Nutr. 2001;4(6):1253-65.