Comparar métodos

	NOVA Food Classification	Dietary Pattern Analysis
Área	Food Agriculture Studies	Food Agriculture Studies
Família	Process / pipeline	Process / pipeline
Ano de origem	2019	2002
Autor original	Carlos A. Monteiro and colleagues (University of Sao Paulo)	Frank B. Hu; P. K. Newby & Katherine L. Tucker
Tipo	Food-processing classification pipeline for diet and food-system analysis	Multivariate pipeline for deriving empirical dietary patterns from food intake
Fonte seminal	Monteiro, C. A., Cannon, G., Levy, R. B., Moubarac, J.-C., Louzada, M. L. C., Rauber, F., Khandpur, N., Cediel, G., Neri, D., Martinez-Steele, E., Baraldi, L. G., & Jaime, P. C. (2019). Ultra-processed foods: what they are and how to identify them. Public Health Nutrition, 22(5), 936-941.	Hu, F. B. (2002). Dietary pattern analysis: a new direction in nutritional epidemiology. Current Opinion in Lipidology, 13(1), 3-9.
Outros nomes	NOVA, NOVA classification, Ultra-Processed Food Classification, NOVA food processing classification	Empirical Dietary Patterns, A Posteriori Dietary Patterns, Data-Driven Dietary Patterns, Eating Pattern Analysis
Relacionados	4	4
Resumo	The NOVA classification groups foods not by their nutrient content but by the nature, extent, and purpose of the industrial processing they undergo, sorting all items into four groups: unprocessed or minimally processed foods, processed culinary ingredients, processed foods, and ultra-processed foods. Developed by Carlos Monteiro and colleagues at the University of Sao Paulo, NOVA introduced ultra-processed foods (UPF) as a category — industrial formulations made largely from substances extracted from foods plus additives — and argued that this processing dimension, rather than nutrient profile alone, is central to diet and health. The 2019 paper Ultra-processed foods: what they are and how to identify them gives the operational definitions, and the share of dietary energy from ultra-processed foods has become a widely used exposure in nutrition and food-system research.	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.