Introduction to Human Nutrition

284 Introduction to Human Nutrition

quality of a food, for example the biological origin,
method of cooking and conservation, and techno-
logical treatments.
Other problems related to the method of expres-
sion of nutrients may arise from the long-standing
convention of using protein values derived by apply-
ing a factor to measured total nitrogen values and
from the calculation of energy values using energy
conversion factors. Calculation of total carbohydrate
content by difference as opposed to the sum of the
individual carbohydrates is no longer the norm. The
bases of expression in databases are the most com-
monly used units (such as g) per 100 g of edible
portion. In some instances unit per 100 g of dry mass
is presented, or unit per 100 ml. However, some tables
list nutrient content per serving size or household
measure, either as purchased or as prepared.

Quality of data

The quality of food composition data is critical for
the accuracy of the estimates of compounds in food.
In particular, analytical data obtained from scientifi c
literature and laboratory reports can be evaluated for

quality. The quality of the analytical data is affected

by various factors, including how the food samples

were selected (sampling plan) and handled before

analysis, use of appropriate analytical method and

analytical quality control, and adequacy of number of

samples to address variability. In addition, complete

food description and identifi cation of the compo-

nents analyzed are also important.

The data quality evaluation system developed by

the USDA is based on the evaluation of fi ve catego-

ries: sampling plan, sample handling, analytical

method, analytical quality control, and the number of

individual samples analyzed. Detailed documentation

of all the steps within each category is important

for evaluating that category. Each category gets a

maximum rating of 20 rating points. A quality index

(QI) is generated by combining points of all the fi ve

categories and confi dence codes (CCs) ranging from

A to D indicating relative confi dence in the data

quality are assigned. These confi dence codes could be

released with the data and thus provide an indication

of the data quality to the user of the data. Confi dence

code “A” indicates data of highest quality, while con-

fi dence code “D” suggests data of questionable quality.

These procedures can be used to guide the planning

and conducting of food analysis projects.

11.4 Information required on sources of

data in tables

It is important to have information on the source of

the data in a food composition table to be able to

check its appropriateness for the study and to confi rm

its authenticity. The four major categories of sources

of data are:

● primary publications, e.g., peer-reviewed articles in

scientifi c literature

● secondary publications, e.g., reviews or published

compilations with compositional data

● unpublished reports ranging from analytical records

to documents prepared with limited circulation,

e.g., confi dential reports for clients or internal use

within a company

● unpublished analytical data that can be either spe-

cifi cally commissioned analyses for the generation

of nutrient data or analytical data that were not

particularly generated for the purpose of generat-

ing food composition data.

Table 11.5 Example of an INFOODS tagname and its defi nition for a
food component