Abstract:
Evidence-based knowledge of the relationship between foods and nutrients is needed
to inform dietary-based guidelines and policy. Proper and tailored statistical methods to analyse
food composition databases (FCDBs) could assist in this regard. This review aims to collate the
existing literature that used any statistical method to analyse FCDBs, to identify key trends and
research gaps. The search strategy yielded 4238 references from electronic databases of which
24 fulfilled our inclusion criteria. Information on the objectives, statistical methods, and results was
extracted. Statistical methods were mostly applied to group similar food items (37.5%). Other aims
and objectives included determining associations between the nutrient content and known food
characteristics (25.0%), determining nutrient co-occurrence (20.8%), evaluating nutrient changes over
time (16.7%), and addressing the accuracy and completeness of databases (16.7%). Standard statistical
tests (33.3%) were the most utilised followed by clustering (29.1%), other methods (16.7%), regression
methods (12.5%), and dimension reduction techniques (8.3%). Nutrient data has unique characteristics
such as correlated components, natural groupings, and a compositional nature. Statistical methods
used for analysis need to account for this data structure. Our summary of the literature provides a
reference for researchers looking to expand into this area.
