| Food Composition Data: A User's Perspective (1987) |
|A system for evaluating the quality of published nutrient data: Selenium, a test case|
In 1980 the need for objective criteria for evaluating food composition data was recognized by USDA workers at the Nutrient Composition Laboratory of the Agricultural Research Service and the Nutrient Data Research Branch of the Human Nutrition Information Service. Discussions led to the development of data quality criteria which were used to evaluate iron data for publication of the provisional table Iron Content of Food .
The various studies for specific foods were evaluated according to criteria in three categories: (a) documentation of analytical method, (b) sample handling and appropriateness of analytical method, and (c) (analytical) quality control. Scores for these criteria led to the assignment of a CC, which appeared in the iron table adjacent to the mean iron concentration. For the first time, users of a nutrient composition table were provided with a measure or degree of confidence in the mean value for that particular food. Asterisks attached to a CC indicated either a limited number of sources or the extent of variability.
Recently, Stewart  reiterated the importance of evaluating nutrient data for inclusion in data bases. He recommended several critical activities that contribute to the generation of highquality analytical data on foods, including (a) development of appropriate validated analytical methods, (b) use of sound food-sampling techniques to ensure representativeness of samples, and (c) use of appropriate quality-control systems in conjunction with validated methodologies to ensure the production of validated composition data.
Although we know of no other similar effort in the field of food composition data, some researchers in other fields have been concerned with systems for the evaluation of data quality. W. Mabey et al.  have recently published a similar system of criteria to be used for the evaluation of quantitative data to be included in a data base for environmental fate assessment. For each of four major categories, specific check-lists of criteria were presented to permit a thorough evaluation of each datum, resulting in the derivation of a data quality index. This index indicates to the data-base user that an objective evaluation of data quality has been done and should help to educate users and data generators and improve the overall excellence of the data.