Login / Signup

Comparison of the nutritional content of products, with and without nutrient claims, targeted at children in Brazil.

Vanessa Mello RodriguesMike RaynerAna Carolina FernandesRenata Carvalho de OliveiraRossana Pacheco da Costa ProençaGiovanna Medeiros Rataichesck Fiates
Published in: The British journal of nutrition (2016)
Many children's food products highlight positive attributes on their front-of-package labels in the form of nutrient claims. This cross-sectional study investigated all retailed packaged foods (n 5620) in a major Brazilian supermarket, in order to identify the availability of products targeted at children, and to compare the nutritional content of products with and without nutrient claims on labels. Data on energy, carbohydrate, protein, fibre, Na and total and SFA content, along with the presence and type of nutrient claims, were obtained in-store from labels of all products. Products targeted at children were identified, divided into eight food groups and compared for their nutritional content per 100 g/ml and the presence of nutrient claims using the Mann-Whitney U test (P<0·05). Of the 535 food products targeted at children (9·5 % of all products), 270 (50·5 %) displayed nutrient claims on their labels. Children's products with nutrient claims had either a similar or worse nutritional content than their counterparts without nutrient claims. The major differences among groups were found in Group 8 (e.g. sauces and ready meals), in which children's products bearing nutrient claims had higher energy, carbohydrate, Na and total and SFA content per 100 g/ml than products without nutrient claims (P<0·05). This suggests that, to prevent misleading parents who are seeking healthier products for their children, the regulation on the use of nutrient claims should be revised, so that only products with appropriate nutrient profiles are allowed to display them.
Keyphrases
  • health insurance
  • young adults
  • risk assessment
  • machine learning
  • mental health
  • mass spectrometry
  • human health
  • high resolution
  • data analysis
  • big data
  • single molecule
  • high speed
  • atomic force microscopy