Recipe calculation

Thousands of different manufactured and composite foods (foods containing more than one ingredient) are available from supermarkets today. These foods contribute significantly to food and nutrient intake. Information on the nutritional composition of composite foods is often based on recipes and the composition of the ingredients of the composite food. Data derived from recipe calculations are prone to errors because of the assumptions made on changes in nutrient concentration that can occur during processing.

You can read more about recipe calculation in:

Greenfield and Southgate, Appendix 6

Additional information:

- Ian D. Unwin (2000), EUROFOODS

Guidelines for Recipe Information

Management. J of Food Composition and

Analysis 13 (4): 745-754

- A. Bognár and J. Piekarski (2000),

Guidelines for Recipe Information and

Calculation of Nutrient Composition of

Prepared Foods (Dishes). J of Food

Composition and Analysis 13 (4): 391-410

Food description

Accurate use of food composition data requires that the food items are correctly identified. Consumers in different parts of the world often give foods different names and the same names are occasionally used for different foods. Standardized food description is therefore needed to identify foods clearly and to identify the type of preparation or processing used. See for example the following striking differences in nomenclature and imagine what goes wrong if foods are not clearly and uniformly described.

You can read more about the selection of foods, food description and classification in: Greenfield and Southgate, chapter 3: selection of foods.

Additional information:

www.langual.org (LanguaL stands for "Langua aLimentaria" or "language of food". It is an automated method for describing, capturing and retrieving data about food.)

Sample handling

After a food sample is taken, it will be prepared for analysis and will be transported to the laboratory and stored for analysis. During this stage the sample should be protected from changes in composition.

Errors that could arise during this phase are for example:

- Contamination (with for example iron) while cutting, mincing or grinding a food sample.

- Loss of moisture during transportation. This can affect the concentration of all nutrients.

- Exposure to light could influence the riboflavin contents.

- etc

You can read more about sampling in:

- Greenfield and Southgate, chapter 5: Sampling.

- D.A.T. Southgate. Data quality in sampling, analysis and compilation. Journal of Food Composition and analysis (2002), 15: 507-513.

Analytical method

The methods chosen for analysis must be appropriate for the type of food that is analyzed as different methods can yield different results.

For example, the appropriate method for fat determination depends on the type of food; Animal fats have to be extracted with a different method than plant fats.

Choosing the right analytical methods and related data quality issues are the topic of the cases in this learning material.

You can read more about analytical methods in: Greenfield and Southgate, chapter 7: Review of methods of analysis.

Analytical performance

When the methods that are used for the analysis of the nutrient contents of the foods are not executed properly, this can introduce errors. Even if the same methodological principles are followed, subtle differences in procedure and in calibration materials could still cause large differences in outcome.

An appropriate quality assurance system within laboratories is therefore important.

The cases in this learning material will give also some attention to such quality assurance systems.

You can read more about analytical performance in:

- Greenfield and Southgate, chapter 8: Assuring the quality of analytical data.

- D.A.T. Southgate. Data quality in sampling, analysis and compilation. Journal of Food Composition and analysis (2002), 15: 507-513.

Sampling

The primary objectives in sampling are to collect food samples that are representative of what is eaten by the population and to ensure that changes in composition do not take place between collection and analysis. Inadequate sampling is a major source of error in food composition databases

Examples of sampling errors that can occur are:

- Number of collected samples is too small

- The sample size unit is too small

- Only foods with a certain characteristic are (accidentally) sampled (e.g. crops from one geographical region)

Therefore an adequate sampling protocol is essential to reduce errors. The sampling protocol describes the sampling process and needs to take into account any seasonal, regional or other variations to determine number of samples, sampling handling, sample preparation etc.

You can read more about sampling in: Greenfield and Southgate, chapter 5: Sampling.

Additional information

M.A.M. Galeazzi, D.M. Lima, F.A.B. Colugnati. R.M. Padovani, and D.B. Rodriguez- Amaya. Sampling Plan for the Brazilian TACO Project. Journal of food composition and analysis (2002). 15: 499-505

This paper provides a good description of a sample plan

Number of samples

The number of samples that is needed depends on the variability of the composition of the sample. For example, many micronutrients (especially vitamins) show greater variability than for example macronutrients like proteins. So in many cases, the number of samples required to determine the vitamin contents needs to be larger than the number of samples required for the assessment of protein contents.

When the number of samples is too small, it is likely to get a very biased estimate of the nutrient contents in the food. Therefore, the number of samples could influence the quality of data in FCDB.

You can read more about sampling

in: Greenfield and Southgate, chapter 5: Sampling and Appendix 2 (calculation of sample size).

More information

J.H. Holden, C.S. Davis(1995). Strategies for sampling: the assurance of representative values. In: Proceedings of the First International Food Database Conference (H Greenfield, Ed), pp 105-117. AOAC, Washington.

Title of page

This module will focus on how nutrient analysis methods could influence quality of Food Composition data.

Click on each part of the diagram for more information. Note that through the wole module the red dashed line indicates that you can click on that element for more informatio n.

Several stages in the development of a Food Composition Database can influence the quality of the final data. For more information about how several stages in a FCDB program could influence data quality you can use the information buttons and study additional literature.