Statistical methods for the analysis of food composition databases
dc.contributor.author | Balakrishna, Yusentha | |
dc.contributor.author | Manda, S.O.M. (Samuel) | |
dc.contributor.author | Mwambi, Henry | |
dc.contributor.author | Van Graan, Averalda | |
dc.contributor.email | samuel.manda@up.ac.za | en_US |
dc.date.accessioned | 2023-09-29T11:49:29Z | |
dc.date.available | 2023-09-29T11:49:29Z | |
dc.date.issued | 2022-05-25 | |
dc.description | DATA AVAILABILITY STATEMENT : No new data were created or analysed in this study. Data sharing is not applicable to this article. | en_US |
dc.description.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. | en_US |
dc.description.department | Statistics | en_US |
dc.description.librarian | am2023 | en_US |
dc.description.sponsorship | The South African Medical Research Council. | en_US |
dc.description.uri | https://www.mdpi.com/journal/nutrients | en_US |
dc.identifier.citation | Balakrishna, Y.; Manda, S.; Mwambi, H.; van Graan, A. Statistical Methods for the Analysis of Food Composition Databases: A Review. Nutrients 2022, 14, 2193. https://DOI.org/10.3390/nu14112193. | en_US |
dc.identifier.issn | 2072-6643 | |
dc.identifier.other | 10.3390/nu14112193 | |
dc.identifier.uri | http://hdl.handle.net/2263/92621 | |
dc.language.iso | en | en_US |
dc.publisher | MDPI | en_US |
dc.rights | © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license. | en_US |
dc.subject | Nutrient database | en_US |
dc.subject | Review | en_US |
dc.subject | Statistical methods | en_US |
dc.subject | Lustering | en_US |
dc.subject | Dimension reduction | en_US |
dc.subject | Regression | en_US |
dc.subject | SDG-02: Zero hunger | en_US |
dc.subject | Food composition database (FCDB) | en_US |
dc.title | Statistical methods for the analysis of food composition databases | en_US |
dc.type | Article | en_US |