Effects of Nutrient Density and exogenous enzymesin Starter Diet on Performance, Intestinal Microflora, Gut Morphology and Immune Response of Broiler Chickens

ABSTRACT A total of 810 one-day-old, straight-run broilers were used to evaluate the effects of dietary nutrient density and feed additives included in the starter diet on their performance, intestinal microbiota, gut morphology, and immune response. A 3×3 factorial arrangement with three nutrient densities (100, 103.75 and 107.5%, as recommended) and three feed additives (no additives, 0.5 g/kg diet Maxi-Gen, and Maxi-Gen + Superzyme + Bio-Phytase at the rate of 0.5, 0.25 and 0.1 g/kg diet, respectively), fed from 1 to 10 d of age. Similar commercial corn-soy grower and finisher diets fed to all birds from 10-24 and 24-42 d of age, respectively. There was higher (p 0.05) body weight gain and lower (p 0.05) feed conversion ratio in chicks fed starter diet with 107.5% nutrient density and Maxi-Gen with or without exogenous enzymes compared with those fed control diet at 10 and 42 d of age. Lactobacilli and Bifidobacteria counts in the cecal content were increased linearly as dietary nutrient density increased in 10-d-old birds (p 0.05). Higher duodenal and jejunal villus height and villus height to crypt depth ratio (p 0.05) were measured in the birds fed the starter diets with 103.75% and 107.5% nutrient density at 5 and 10 d of age. Total anti-SRBC and IgM titers were significantly higher in the broilers fed the 107.5% nutrient density diet containing feed additives at 35 day of age. It is concluded that higher nutrient density and the inclusion of feed additives in the starter diet may improve the growth performance, gut morphology, and immune response of broiler chickens.(AU)

Effects of Nutrient Density and exogenous enzymesin Starter Diet on Performance, Intestinal Microflora, Gut Morphology and Immune Response of Broiler Chickens

ABSTRACT A total of 810 one-day-old, straight-run broilers were used to evaluate the effects of dietary nutrient density and feed additives included in the starter diet on their performance, intestinal microbiota, gut morphology, and immune response. A 3×3 factorial arrangement with three nutrient densities (100, 103.75 and 107.5%, as recommended) and three feed additives (no additives, 0.5 g/kg diet Maxi-Gen, and Maxi-Gen + Superzyme + Bio-Phytase at the rate of 0.5, 0.25 and 0.1 g/kg diet, respectively), fed from 1 to 10 d of age. Similar commercial corn-soy grower and finisher diets fed to all birds from 10-24 and 24-42 d of age, respectively. There was higher (p 0.05) body weight gain and lower (p 0.05) feed conversion ratio in chicks fed starter diet with 107.5% nutrient density and Maxi-Gen with or without exogenous enzymes compared with those fed control diet at 10 and 42 d of age. Lactobacilli and Bifidobacteria counts in the cecal content were increased linearly as dietary nutrient density increased in 10-d-old birds (p 0.05). Higher duodenal and jejunal villus height and villus height to crypt depth ratio (p 0.05) were measured in the birds fed the starter diets with 103.75% and 107.5% nutrient density at 5 and 10 d of age. Total anti-SRBC and IgM titers were significantly higher in the broilers fed the 107.5% nutrient density diet containing feed additives at 35 day of age. It is concluded that higher nutrient density and the inclusion of feed additives in the starter diet may improve the growth performance, gut morphology, and immune response of broiler chickens.

Relationship between color and tannin content in sorghum grain: application of image analysis and artificial neural network

The relationship between sorghum grain color and tannin content was reported in several references. In this study, 33 phenotypes of sorghum grain differing in seed characteristics were collected and analyzed by Folin-Ciocalteu method. A computer image analysis method was used to determine the color characteristics of all 33 sorghum phenotypes. Two methods of multiple linear regression and artificial neural network (ANN) models were developed to describe tannin content in sorghum grain from three input parameters of color characteristics. The goodness of fit of the models was tested using R², MS error, and bias. The computer image analysis technique was a suitable method to estimate tannin through sorghum grain color strength. Therefore, the color quality of the samples was described according three color parameters: L* (lightness), a* (redness - from green to red) and b* (blueness - from blue to yellow. The developed regression and ANN models showed a strong relationship between color and tannin content of samples. The goodness of fit (in terms of R²), which corresponds to training the ANN model, showed higher accuracy of prediction of ANN compared with the equation established by the regression method (0.96 vs. 0.88). The ANN models in term of MS error showed lower residuals distribution than that of regression model (0.002 vs. 0.006). The platform of computer image analysis technique and ANN-based model may be used to estimate the tannin content of sorghum.(AU)

Relationship between color and tannin content in sorghum grain: application of image analysis and artificial neural network

The relationship between sorghum grain color and tannin content was reported in several references. In this study, 33 phenotypes of sorghum grain differing in seed characteristics were collected and analyzed by Folin-Ciocalteu method. A computer image analysis method was used to determine the color characteristics of all 33 sorghum phenotypes. Two methods of multiple linear regression and artificial neural network (ANN) models were developed to describe tannin content in sorghum grain from three input parameters of color characteristics. The goodness of fit of the models was tested using R², MS error, and bias. The computer image analysis technique was a suitable method to estimate tannin through sorghum grain color strength. Therefore, the color quality of the samples was described according three color parameters: L* (lightness), a* (redness - from green to red) and b* (blueness - from blue to yellow. The developed regression and ANN models showed a strong relationship between color and tannin content of samples. The goodness of fit (in terms of R²), which corresponds to training the ANN model, showed higher accuracy of prediction of ANN compared with the equation established by the regression method (0.96 vs. 0.88). The ANN models in term of MS error showed lower residuals distribution than that of regression model (0.002 vs. 0.006). The platform of computer image analysis technique and ANN-based model may be used to estimate the tannin content of sorghum.