Adsorption of aflatoxin B1 by different antimycotoxin additives: bentonite, clinoptilolite, and beta-glucans extracted from yeast cell wall.

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 B1 (AFB1) using the isothermal models of Freundlich, Langmuir, and BET. The additives were submitted to an in vitro adsorption experiment with AFB1 (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 AFB1, 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 qm 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 qm 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.

Prediction of fumonisins B1 and B2 in corn distiller's dried grains with solubles through near-infrared reflectance spectroscopy.

BACKGROUND: Distiller's dried grains with solubles (DDGS) are coproducts of the biofuel industries that use corn as raw material. This cereal is commonly contaminated by mycotoxins, including fumonisins (FBs), which can pose a serious health threat to humans and animals. Corn DDGS are typically used as a protein-rich animal feed. As mycotoxins from the original cereal grains become concentrated in DDGS, mycotoxicological monitoring is highly required before their use as ingredient in the industry. RESULTS: This work aimed to develop a methodology for predicting fumonisins B1 (FB1 ) and B2 (FB2 ) in corn DDGS using near-infrared reflectance spectroscopy (NIRS) technology associated with chemometric methods. One hundred and ninety corn DDGS samples originating from Brazilian ethanol plants and feed mills were included in this assessment. Two datasets were created: one for calibration (132 samples) and another for external validation (58 samples). Partial least squares regression and a cross-validation approach were applied to build the models. Liquid chromatography coupled to tandem mass spectrometry was used as the reference methodology. Calibration results of correlation coefficient and residual prediction deviation for FB1 and FB2 were, respectively, 0.90 and 0.88; and 2.16 and 2.06. CONCLUSION: Values of the external validation dataset were compared and no statistical difference was found between groups, indicating a satisfactory predictive ability and confirming the potential of NIRS to predict fumonisins in corn DDGS. © 2022 Society of Chemical Industry.