Dietary Pattern Analysis
Dietary pattern analysis is the nutritional-epidemiology application of multivariate statistics that identifies how foods are actually eaten together, summarizing the whole diet into a few empirical patterns rather than studying single nutrients in isolation. Introduced as a research direction by Frank Hu in his 2002 Current Opinion in Lipidology review and surveyed methodologically by Newby and Tucker in 2004, the approach takes a matrix of food-group intakes and applies factor (principal component) analysis, cluster analysis, or reduced-rank regression to extract a posteriori patterns such as a 'prudent' pattern rich in fruits, vegetables, and whole grains and a 'Western' pattern high in red meat and refined foods. While the underlying algebra is generic principal component or cluster analysis, what makes this a distinct method is its substantive construction: the input is the food-group intake matrix of the whole diet, and the output is interpretable eating patterns linked to disease.
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Methodenkaart
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Bronnen
- Hu, F. B. (2002). Dietary pattern analysis: a new direction in nutritional epidemiology. Current Opinion in Lipidology, 13(1), 3-9. DOI: 10.1097/00041433-200202000-00002 ↗
- Newby, P. K., & Tucker, K. L. (2004). Empirically derived eating patterns using factor or cluster analysis: a review. Nutrition Reviews, 62(5), 177-203. DOI: 10.1111/j.1753-4887.2004.tb00040.x ↗
Deze pagina citeren
ScholarGate. (2026, June 23). Dietary Pattern Analysis (A Posteriori Empirical Dietary Patterns). ScholarGate. https://scholargate.app/nl/food-agriculture-studies/dietary-pattern-analysis
Welke methode?
Plaats deze methode naast haar naaste verwanten en lees ze naast elkaar — de bibliotheek legt de boeken op tafel; de keuze is aan u.
- Individual Dietary Diversity ScoreFood Agriculture Studies↔ vergelijken
- Multiple-Pass 24-Hour Dietary RecallFood Agriculture Studies↔ vergelijken
- NOVA Food ClassificationFood Agriculture Studies↔ vergelijken
- Weighed Food RecordFood Agriculture Studies↔ vergelijken
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