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Relationship between the Fungal Incidence, Water Activity, Humidity, and Aflatoxin Content in Maize Samples from the Highlands and Coast of Ecuador.

Héctor Abel PalaciosAndrieli StefanelloMargarita Susana García GavilánezDicke Alejandro Castro DemeraMarcelo Valle GarciaWilson Arturo Vásquez CastilloMarcelo Alejandro Almeida MarcanoIván Rodrigo Samaniego MaiguaMarina Venturini Copetti
Published in: Toxins (2022)
This study evaluated the fungal incidence through direct plating in Agar Dichloran Glycerol, and the presence of aflatoxins in maize samples from the Highlands and Coast of Ecuador by HPLC, investigating the influence of the temperature, altitude, water activity, and humidity of the collection regions on the maize samples' contamination using Principal Components Analysis (PCA). The overall kernel infection by fungi was usually lower in samples from the Highlands, and no aflatoxins or Aspergillus series Flavi were detected in the samples from this region. In the coastal samples, Aspergillus sp. were isolated from all samples, while the potentially aflatoxigenic A. Flavi contaminated about 80% of them. Aflatoxins were present in 50% of these samples, in ranges from 0.42 to 107.69 µg/kg. PCA was able to segregate the samples according to their collection region, and showed that the maximum and minimum temperatures are closely and positively related to the presence of A. Flavi . A highly positive relationship was also observed between the water activity of the sample and aflatoxin contamination. On the other hand, the altitude had a very strong-but negative-relationship with the variables studied. This study is relevant because data regarding fungi and aflatoxin occurrence, as well the main factor influencing the contamination of Ecuadoran maize, are scarce; it clearly shows that aflatoxins are a hazard present in maize from the Ecuadorian Coast but not the Highlands.
Keyphrases
  • risk assessment
  • drinking water
  • heavy metals
  • human health
  • health risk
  • risk factors
  • ms ms
  • mass spectrometry
  • climate change
  • deep learning
  • big data