This study describes the development of a database, called MilkyBase, of the biochemical composition of human milk. The data were selected, digitized and curated partly by machine-learning, partly manually from publications. The database can be used to find patterns in the milk composition as a function of maternal-, infant- and measurement conditions and as a platform for users to put their own data in the format shown here. The database is an Excel workbook of linked sheets, making it easy to input data by non-computationally minded nutritionists. The hierarchical organisation of the fields makes sure that statistical inference methods can be programmed to analyse the data. Uncertainty quantification and recording dynamic (time-dependent) compositions offer predictive potentials.

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This study examined how Brazilian schoolchildren identified, classified, and labeled foods and beverages. Semi-structured interviews were conducted with 133 schoolchildren aged 7 to 10 years old from a public school located in southern Brazil in 2015. A set of cards with pictures of 32 food and beverage items from the web-based Food Intake and Physical Activity of Schoolchildren tool (Web-CAAFE) were used. Participants identified each item, formed groups for them based on similarity, and assigned labels for those groups. Student’s t-tests and analysis of variance (ANOVA) tests were used to verify the mean difference between the groups of items. K-means cluster analysis was applied to identify similar clusters. Schoolchildren made an average of 9.1 piles of foods and beverages that they thought were similar (±2.4) with 3.0 cards (±1.8) each. Five groups were identified: meats, snacks and pasta, sweets, milk and dairy products, and fruits and vegetables. The most frequently used nomenclature for labeling groups was taxonomic-professional (47.4%), followed by the specific food item name (16.4%), do not know/not sure (13.3%), and evaluative (health perception) (8.8%). The taxonomic-professional category could be applied to promote improvements in the identification process of food and beverage items by children in self-reported computerized dietary questionnaires.