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Estimating Food Composition Data

 Updated 2015-08-15

 Deriving food composition data for food components where only few analytical values exist


Compilation of food composition data often leaves the data compiler with the headache of many missing values, especially for foods that are rarely analysed or eaten. In order to avoid missing values, the compiler is therefore forced to estimate or guess the value ("guestimate"). It is important that the estimated values are created on a sound basis and documented in such a way that the estimation can be tracked.
One of the basic papers on the estimation of nutrient values is

  • Schakels S., Buzzard S., Gebhardt S.E.:
    Procedures for Estimating Nutrient Values for Food Composition Databases
    Journal of Food Composition and Analysis 10, 102–114 (1997)

It gives a good and thorough description on how to estimate nutrient values when analytical data are not available. The authors basically divide the subject into six main categories

  • Using Nutrient Values from a Different, but Similar, Food
  • Calculating Nutrient Values from a Different Form of the Same Food
  • Calculating Nutrient Values from Other Components in the Same Food
  • Calculating Nutrient Values from Household Recipes or Commercial Product Formulations for Multicomponent Foods
  • Converting Nutrient Values from Nutrient Label Information of a Commercial Food Product
  • Calculating Nutrient Values from a Product Standard

and give an in-depth description of the process of validating the estimated values as well as documenting and referencing estimated values.

The Biologically or Technically "Standardised" foods

For some foods/food groups there are reasonable and sound solutions to overcome the challenge of estimating values. These foods are characterized by the fact that the amounts of some components is related to the content of other components. Especially in foods of animal origin such relationships can be established without much effort using using common mathematical/statistical tools, like linear regression. For example, there are completely linear relationships between the dry matter (moisture) content and the fat content in meat - a relationship that is widely used in the meat industry as the fat content can be estimated by analysing the dry matter content in the meat, a much simpler (and cheaper method).

The biologically or technically "standardized" foods are foods that from nature or via processing contain certain amounts of components - and often - there is an (internal) relationship between several of the components.

The foods in this category are raw and prepared meats, i.e. meat from animals like cattle (beef, veal), pigs (pork) and sheep (sheep, lamb). Similarly, also dairy products (milk and cheeses) show such relationships. Less apparent, but still existing are also the relations between components in fish.
The challenge is to find these internal relations in the foods, e.g. with the help of linear regression models (single or nultiple). On the underlying pages, some of these relations are described.


The fact that we are working with calculated/estimated data values and not actual results of chemical analysis calls for extreme caution. First of all, the calculated values must represent values that are realistic in the compiler's environment, i.e. the relationships from which the values have been calculated should be derived from analytical values representative for the compilers' environment as far as possible.
Furthermore, the relationships/hypotheses on which the estimations are based should be checked by actual chemical analysis of samples, if possible. 


  • Schakels S., Buzzard S., Gebhardt S.E.:
    Procedures for Estimating Nutrient Values for Food Composition Databases
    Journal of Food Composition and Analysis 10, 102–114 (1997)


DTU National Food Institute published new report on flours, grains and kernels.

DTU National Food Institute publishes new analytical report on macro- and micronutrients in flour, grains and seeds on the Danish market. See DTU Natinal Food Institute website.
EFSA to share data on open-access platform

EFSA takes major step towards fully open data organisation by publishing the scientific data it uses for EU-wide monitoring programmes, surveys and risk assessments, including food composition data from 7 EU Member Countries. See EFSA website.
LanguaL™ 2017 published

The new release of LanguaL™ - version 2017 - published. See the LanguaL™ website.
New release of the Swedish food database

A new release of the Swedish food database published. See the Swedish National Food Administration website.