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Adsorption of aflatoxin B 1 by different antimycotoxin additives: bentonite, clinoptilolite, and beta-glucans extracted from yeast cell wall.

Luara Medianeira de Lima SchlösserCristina Tonial SimõesJanine Alves SarturiCristiane Rosa da SilvaIsadora Fabris LaberDison Stracke Pfingsten FrancoCarlos Augusto Mallmann
Published in: Mycotoxin research (2023)
The present study aims to evaluate and compare antimycotoxin additives (AMAs) composed of bentonite (AMA 1), clinoptilolite (AMA 2), and beta-glucans extracted from yeast cell wall (AMA 3), with respect to their ability to bind Aflatoxin B 1 (AFB 1 ) using the isothermal models of Freundlich, Langmuir, and BET. The additives were submitted to an in vitro adsorption experiment with AFB 1 (0.05-4 mg L -1 ), using solutions of pH 3 and pH 6, with an inclusion rate of 0.5%, and analyzed by HPLC-MS/MS. At pH 3, for the seven concentrations evaluated, AMA 1 obtained adsorption rates (99.69 to 99.98%) higher (p < 0. 05) than the other AMAs, which were from 82.97 to 88.72% (AMA 2) and from 79.43 to 89.32% (AMA 3). At pH 6, in concentrations of 1, 2, and 4 mg L -1 of AFB 1 , AMA 1 obtained higher (p < 0.05) adsorption results (97.86 to 99.86%) than AMA 2 (91.98 to 96.12%) and AMA 3 (87.56 to 93.50%). The Freundlich model best fitted the AMA 1 adsorption data. For the other additives, the Langmuir model obtained the best fit, demonstrating q m of 8.6 mg g -1 at pH 3 and 2.3 mg g -1 at pH 6 for AMA 2; and for AMA 3, with q m of 3.4 mg g -1 at pH 3 and 2.3 mg g -1 at pH 6. The isotherm models work as an effective tool to describe the adsorption process whereas the AMA adsorption capacity varies as a function of product composition, pH, and mycotoxin content.
Keyphrases
  • cell wall
  • ms ms
  • aqueous solution
  • machine learning
  • artificial intelligence
  • saccharomyces cerevisiae
  • electronic health record
  • high performance liquid chromatography
  • liquid chromatography
  • data analysis