方法对比

	Nutrient Profiling Model	Dietary Pattern Analysis
领域	Food Agriculture Studies	Food Agriculture Studies
方法族	Process / pipeline	Process / pipeline
起源年份	2005	2002
提出者	Mike Rayner, Peter Scarborough & Tim Lobstein (UK FSA/Ofcom model); Public Health Nutrition framework	Frank B. Hu; P. K. Newby & Katherine L. Tucker
类型	Scoring pipeline classifying foods by nutritional composition	Multivariate pipeline for deriving empirical dietary patterns from food intake
开创性文献	Scarborough, P., Rayner, M., & Stockley, L. (2007). Developing nutrient profile models: a systematic approach. Public Health Nutrition, 10(4), 330-336.	Hu, F. B. (2002). Dietary pattern analysis: a new direction in nutritional epidemiology. Current Opinion in Lipidology, 13(1), 3-9.
别名	Nutrient Profiling, UK Ofcom/FSA Nutrient Profile Model, WXYfm Model, Nutrient Profile Scoring	Empirical Dietary Patterns, A Posteriori Dietary Patterns, Data-Driven Dietary Patterns, Eating Pattern Analysis
相关	4	4
摘要	Nutrient profiling is the science of categorising foods according to their nutritional composition for reasons related to preventing disease and promoting health. A nutrient profiling model operationalises this idea as a transparent scoring algorithm: each food is awarded points for components considered detrimental in excess (energy, saturated fat, sugars, sodium) and points for beneficial components (fruit, vegetable and nut content, fibre, protein), and the net score is thresholded to classify the food as 'healthier' or 'less healthy'. The best-known example is the UK Food Standards Agency / Ofcom model (the WXYfm model developed by Rayner, Scarborough and colleagues), adopted in 2007 to restrict television advertising of less-healthy foods to children and later adapted by the WHO and as the underlying engine of front-of-pack schemes. Scarborough, Rayner and Stockley set out the systematic, transparent development process that distinguishes a defensible model from an ad hoc one.	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.