Mohammad Hojati; Mohammad Ali Norouzian; Ali Assadi Alamouti; Ahmad Afzalzadeh
Volume 12, Issue 2 , June 2021, , Pages 211-215
Abstract
This study was conducted to compare the efficacy of different feed additives as mycotoxin binders in vitro. Four prevalent aflatoxin-sequestering agents (SAs) including two bentonite clays (common and acid activated bentonite), a yeast cell wall product and an activated charcoal product were evaluated ...
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This study was conducted to compare the efficacy of different feed additives as mycotoxin binders in vitro. Four prevalent aflatoxin-sequestering agents (SAs) including two bentonite clays (common and acid activated bentonite), a yeast cell wall product and an activated charcoal product were evaluated in vitro to verify their capacity for binding aflatoxin B1 (AFB1). The SAs were individually mixed at two different ratios with AFB1 (1:70,000, 1:120,000) and their binding capacity indices were determined. Experimental bentonites showed high adsorption abilities, binding more than 70.00% of the available AFB1. At the 1:70,000 and 1:120,000 aflatoxin binder (AF:B) ratios, acid activated bentonite were sequestered over 87.00 and 99.00% of the AFB1, respectively. Yeast cell wall showed moderate adsorption ability at the 1:120,000 AF:B ratio, adsorbing 47.00 of AFB1. The adsorption ability of activated carbon at two AF:B ratio and yeast cell wall at 1:70,000 AF:B ratio were significantly lower than other binders. The ratio of chemisorption and binding equivalency factor were higher for acid activated bentonite compared to other sequestering agents. Based on the result of this study, it seems that acid activated bentonite could be considered efficient at sequestering the available AFB1, resulting as promising agents for use in animals diet.
Mohammad Ali Norouzian; Hossein Bayatani; Mona Vakili Alavijeh
Volume 12, Issue 1 , March 2021, , Pages 33-37
Abstract
In this study, artificial neural networks (ANNs) were employed to investigate the relationship between locomotion score and production traits. A total number of 123 dairy cows from a free-stall housing farm were used in this study. To compare the effectiveness of the ANNs for the prediction of locomotion ...
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In this study, artificial neural networks (ANNs) were employed to investigate the relationship between locomotion score and production traits. A total number of 123 dairy cows from a free-stall housing farm were used in this study. To compare the effectiveness of the ANNs for the prediction of locomotion score, the multiple linear regression (MLR) model was developed using the eight production traits, body condition score, parity, days in milk, daily milk yield, milk fat percent, milk protein percent, daily milk fat yield, and daily milk protein yield as input variables to predict the locomotion score. The ANN predictions gave a higher coefficient of determination (R2) values with lower mean squared error (MSE) than MLR. The R2 and MSE of the MLR model were 0.53 and 0.36, respectively. However, the ANN model for the same dataset produced much improved results with R2 = 0.80 and MSE = 0.16, respectively. Globally, the results of this study showed that the connectionist network model was a better tool to predict locomotion scores compared to the multiple linear regression.