Statistical methods for the analysis of food composition databases

dc.contributor.authorBalakrishna, Yusentha
dc.contributor.authorManda, S.O.M. (Samuel)
dc.contributor.authorMwambi, Henry
dc.contributor.authorVan Graan, Averalda
dc.contributor.emailsamuel.manda@up.ac.zaen_US
dc.date.accessioned2023-09-29T11:49:29Z
dc.date.available2023-09-29T11:49:29Z
dc.date.issued2022-05-25
dc.descriptionDATA AVAILABILITY STATEMENT : No new data were created or analysed in this study. Data sharing is not applicable to this article.en_US
dc.description.abstractEvidence-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.departmentStatisticsen_US
dc.description.librarianam2023en_US
dc.description.sponsorshipThe South African Medical Research Council.en_US
dc.description.urihttps://www.mdpi.com/journal/nutrientsen_US
dc.identifier.citationBalakrishna, 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.issn2072-6643
dc.identifier.other10.3390/nu14112193
dc.identifier.urihttp://hdl.handle.net/2263/92621
dc.language.isoenen_US
dc.publisherMDPIen_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.subjectNutrient databaseen_US
dc.subjectReviewen_US
dc.subjectStatistical methodsen_US
dc.subjectLusteringen_US
dc.subjectDimension reductionen_US
dc.subjectRegressionen_US
dc.subjectSDG-02: Zero hungeren_US
dc.subjectFood composition database (FCDB)en_US
dc.titleStatistical methods for the analysis of food composition databasesen_US
dc.typeArticleen_US

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