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Can the intake of synthetic food colour Amaranth (INS 123) put the health of Brazilian consumers at risk?

Patrícia da Silva RodriguesAlessandro de Oliveira RiosFlorencia Cladera-Olivera
Published in: Food additives & contaminants. Part A, Chemistry, analysis, control, exposure & risk assessment (2022)
Amaranth is a synthetic red azo dye approved in several countries such as Canada, Australia and Brazil, but banned in the United States. There are few studies evaluating the exposure of the general population to this food colouring substance, in Brazil, specifically, there are virtually no data on its intake. This study aimed to estimate the Theoretical Maximum Daily Intake (TMDI) of the Brazilian population and to quantify Amaranth in foods that contribute the most to its consumption. Data on the presence of Amaranth were correlated with consumption data from National Household Budget Surveys carried out in 2008/2009 and 2017/2018, among people aged ten or older. The results show that the mean TMDI (mg/day) of Amaranth does not exceed the Acceptable Daily Intake (ADI) in any population group, it, however, may get as high as 66% of the ADI among teenagers. For the TMDI balanced by the prevalence of food consumption, that is, considering consumers only (eaters only, rather than the population mean), results show that the amounts can exceed the ADI in all population groups studied. The intake of Amaranth is higher among the younger population (adolescents) reaching up to three times the ADI in the worst-case scenario. The food groups which contribute the most to the intake of Amaranth, are 'juices/artificial juices/reconstituted powdered juice mixes' and 'soft drinks'. Laboratory tests of powdered fruit mixes and soft drinks sold in the city of Porto Alegre (Brazil) show that 17 out of 20 samples tested exceeded the limit set by Brazilian regulations (5 mg/100 mL in the final product). Results show that the intake of Amaranth by the different Brazilian populations may pose a health hazard to several population groups.
Keyphrases
  • weight gain
  • healthcare
  • public health
  • mental health
  • human health
  • young adults
  • risk factors
  • electronic health record
  • big data
  • machine learning
  • cross sectional
  • data analysis
  • middle aged