Cad. Saúde Pública, Rio de Janeiro, xx(x):xxx-xxx, xxx, xxxx 105 Focused Principal Component Analysis: a graphical method for exploring dietary patterns Análise de Componente Principal Focada: um método gráfico para explorar padrões alimentares 1 Programa de Pós-graduação em Saúde Coletiva, Universidade do Vale do Rio dos Sinos, São Leopoldo, Brasil. 2 Instituto de Matemática, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brasil. 3 Programa de Pós-graduação em Edidemiologia, Universidade Federal de Pelotas, Pelotas, Brasil. Correspondence M. T. A. Olinto Programa de Pós-graduação em Saúde Coletiva, Universidade do Vale do Rio dos Sinos. Av. Unisinos 950, C. P. 275, São Leopoldo, RS 93022-000, Brasil. mtolinto@unisinos.br Maria Teresa Anselmo Olinto 1 Raquel Canuto 1 Suzi Camey 2 Denise P. Gigante 3 Ana Maria Baptista Menezes 3 Abstract The aim of the present study was to introduce Focused Principal Component Analysis (FPCA) as a novel exploratory method for providing in- sight into dietary patterns that emerge based on a given characteristic of the sample. To dem- onstrate the use of FPCA, we used a database of 1,968 adults. Food intake was obtained using a food frequency questionnaire covering 26 food items. The focus variables used for analysis were age, income, and schooling. All analyses were carried out using R software. The graphs gener- ated show evidence of socioeconomic inequities in dietary patterns. Intake of whole-wheat foods, fruit, and vegetables was positively correlated with income and schooling, whereas for refined cereals, animal fats (lard), and white bread this correlation was negative. Age was inversely as- sociated with intake of fast-food and processed foods and directly associated with a pattern that included fruit, green salads, and other vegetables. In an easy and direct fashion, FPCA allowed us to visualize dietary patterns based on a given focus variable. Introduction While dietary patterns reflect an individual’s food preferences, they are also influenced by other characteristics, such as economic history, income, schooling and demographic character- istics (sex and age). The statistical methods most commonly used for identifying dietary patterns among popula- tions, or among specific population groups, include data reductions based on a posteriori models, such as cluster analysis and principal component analysis (PCA) 1 . Both clustering and PCA are able to identify underlying structures among different food items, i.e. patterns of re- duction and clustering of the dataset. However, investigating the relationship between the di- etary patterns identified by these methods and population characteristics requires subsequent dependence analysis, which entails resorting to multivariate regression models that include both the dietary pattern and other characteristics of the sample. In Brazil, a limited number of studies have attempted to identify dietary patterns and their association with population characteristics. The findings from these studies are consistent in that they indicate the existence of socioeconomic inequities in the dietary patterns of the popula- tion. Lenz et al. 2 identified five dietary patterns by means of PCA. Of these, three displayed the characteristics of a healthy diet, such as the pres- ARTIGO ARTICLE