Mohaddeseh Esnaashari; Hamed Ahmadi; Farid Shariatmadari; Mostafa Lotfi
Volume 25, Issue 1 , April 2023, , Pages 93-105
Abstract
This study was conducted to investigate the effects of mixer added fat, crude protein and conditioning temperature on the pellet durability index, and electrical energy consumption during feed production using computational modeling tools. A total of 192 broiler feed samples with different levels of ...
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This study was conducted to investigate the effects of mixer added fat, crude protein and conditioning temperature on the pellet durability index, and electrical energy consumption during feed production using computational modeling tools. A total of 192 broiler feed samples with different levels of mixer added fat and crude protein in feed components and different conditioning temperatures to determine the pellet durability index, modified pellet durability index and electrical energy consumption during feed production were used. Multiple linear regression and artificial neural network were used to analyze data. Both models had the ability to predict the value of the pellet durability index, modified pellet durability index and the electrical energy consumption during feed production; but the prediction accuracy of the artificial neural network model was higher than that of the multiple linear regression model for all three outputs. Optimization was done using the artificial neural network model, and in these calculations, in order to achieve the highest possible level of pellet physical quality and the lowest possible level of electrical energy consumption, the crude protein amount was 20-20.5% and the conditioning temperature was predicted to be 85 C. However, the amount of fat was predicted to be 1% for the highest amount of pellet physical quality and 4% for the lowest amount of electrical energy consumption during production. In practical conditions, this model can help in more accurate prediction of electricity consumption and the quality of produced feed in order to achieve the optimal situation in feed production factories.
Mostafa Lotfy; farid shariatmadari; Hamed Ahmadi; Mohsen Sharafi
Volume 21, Issue 2 , July 2019, , Pages 223-232
Abstract
The purpose of this study was to develop multiple linear regression (MLR) model to predict the nitrogen-corrected true metabolizable energy (TMEn) value of wheat bran. The amount of crude fat, ash, crude protein, crude fiber (all used as % of DM) and TMEn (Kcal/kg DM) were measured in 25 wheat bran samples ...
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The purpose of this study was to develop multiple linear regression (MLR) model to predict the nitrogen-corrected true metabolizable energy (TMEn) value of wheat bran. The amount of crude fat, ash, crude protein, crude fiber (all used as % of DM) and TMEn (Kcal/kg DM) were measured in 25 wheat bran samples with 4 replicates. The force-fed method has been used to estimate TMEn and excreta were collected for 48 h. There were significant (P < 0.001) differences in chemical composition and TMEn of wheat bran samples. The average crude fat, ash, crude protein, crude fiber and TMEn content of samples was determined to be 4.80, 5.68, 16.23, 8.60 (all used as % of DM) and 2062 (Kcal/kg DM), respectively. The calculated MLR model to predict the TMEn value (Kcal/kg) based on chemical composition (% of DM) was obtained as follows: TMEn = 2364 + (19×crude protein) + (46.1×crude fat) – (63×crude fiber) – (51.1×ash). The R2 value revealed that developed model could accurately predict the TMEn of wheat bran samples (R2=0.82). Crude fat and crude protein had a positive effect on TMEn, while ash and crude fiber had a negative impact on TMEn. The sensitivity analysis on the model indicated that dietary crude fiber (%) is the most important variable in the TMEn, followed by dietary ash, crude fat and crude protein. The results suggest that the MLR model may be used to accurately estimate the TMEn value of wheat bran from its corresponding chemical composition.