A 6-step auditing process was developed to detect unlikely nutrient values in a Nutrient Composition Data Bank for Foods (NCDBF) in Taiwan. Preference was given to finding errors in the database, rather than to determining significant differences in the biological characteristics of the individual nutrients. There were 239 compositionally similar subgroups categorized within the NCDBF. The coefficient of variation (CV) of nutrient values for each subgroup provided the first-order sorting instrument. Nutrient CVs were ranked in rows for food subgroup (x) and in columns for nutrient type (y) and their product (x,y) in descending order. When the rank was in the top 2 or the product was ≤20, the Excel "cell" was regarded as a "hit". The "hit rate" (2.6%, 777 hits/29,424 pieces of information) of the computerized analysis was verified through an expert panel review to provide a "satisfied hit rate (SHR)" (agreed errors/total food group hits). The mean SHR was 14.9% (range: 1.4%-37.6%) for the various food groups. The computerized process performed with a 38-fold increase in likelihood of error detection compared with what manual assessment alone would have produced. This low-cost approach could be applied in various jurisdictions or with other digitized food composition tables.
ASJC Scopus subject areas
- Food Science