Six hundred Ross 308 male broiler chickens were used to study the effect of licorice extract and the prebiotic, fermacto, on performance, blood metabolites and gastro-intestinal transit time (GTT) of feed in the birds. The birds were fed according to a three phase feeding programme on a starter, grower and finisher diet during the ages of 1 -14 day, 15 -35 days and 35 -49 days of age, respectively. The basic diets during each phase contained either 100 or 95% of recommended digestible amino acid (RDAA) concentrations. The two basic starter diets were divided into five treatment diets: No supplement (control); and supplemented with 2.0 g fermacto/kg; and 2.0 (high); 1.0 (medium) and 0.5 (low) g licorice extract/kg diet. In the grower diets half the levels of these supplements were included, while the two finisher diets were fed without containing any of the experimental supplements. There was not a significant difference in body weight, feed intake and feed conversion ratio between the birds fed the control and the diets supplemented with the prebiotic or the different levels of licorice. However, birds receiving diets containing licorice extract had lighter abdominal fat pads compared to those fed the prebiotic or control diets. Blood cholesterol concentrations decreased significantly in birds receiving the high level of licorice in their starter and grower diets as compared to the control. The GTT of feed in the birds fed diets containing the prebiotic or licorice extract did not differ from that of the birds in the control. A 5% reduction in dietary RDAA concentration caused an increase in feed conversion ratio of chickens on the starter and grower diets and for the total duration of the experiment. ________________________________________________________________________________

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

Accurate knowledge of true digestible amino acid (TDAA) contents of feedstuffs is necessary to accurately formulate poultry diets for profitable production. Several experimental approaches that are highly expensive and time consuming have been used to determine available amino acids. Prediction of the nutritive value of a feed ingredient from its chemical composition via regression methodology has been attempted for many years. The artificial neural network (ANN) model is a powerful method that may describe the relationship between digestible amino acid contents and chemical composition. Therefore, multiple linear regressions (MLR) and ANN models were developed for predicting the TDAA contents of sorghum grain based on chemical composition. A precision-fed assay trial using cecectomized roosters was performed to determine the TDAA contents in 48 sorghum samples from 12 sorghum varieties differing in chemical composition. The input variables for both MLR and ANN models were CP, ash, crude fiber, ether extract, and total phenols whereas the output variable was each individual TDAA for every sample. The results of this study revealed that it is possible to satisfactorily estimate the TDAA of sorghum grain through its chemical composition. The chemical composition of sorghum grain seems to highly influence the TDAA contents when considering components such as CP, crude fiber, ether extract, ash and total phenols. It is also possible to estimate the TDAA contents through multiple regression equations with reasonable accuracy depending on composition. However, a more satisfactory prediction may be achieved via ANN for all amino acids. The R(2) values for the ANN model corresponding to testing and training parameters showed a higher accuracy of prediction than equations established by the MLR method. In addition, the current data confirmed that chemical composition, often considered in total amino acid prediction, could be also a useful predictor of true digestible values of selected amino acids for poultry.

Sorghum grain is an important ingredient in poultry diets. The TMEn content of sorghum grain is a measure of its quality. As for the other feed ingredients, the biological procedure used to determine the TMEn value of sorghum grain is costly and time consuming. Therefore, it is necessary to find an alternative method to accurately estimate the TMEn content. In this study, 2 methods of regression and artificial neural network (ANN) were developed to describe the TMEn value of sorghum grain based on chemical composition of ash, crude fiber, CP, ether extract, and total phenols. A total of 144 sorghum samples were used to determine chemical composition and TMEn content using chemical analyses and bioassay technique, respectively. The values were consequently subjected to regression and ANN analysis. The fitness of the models was tested using R(2) values, MS error, and bias. The developed regression and ANN models could accurately predict the TMEn of sorghum samples from their chemical composition. The goodness of fit in terms of R(2) values corresponding to testing and training of the ANN model showed a higher accuracy of prediction than the equation established by regression method. In terms of MS error, the ANN model showed lower residuals distribution than the regression model. The results suggest that the ANN model may be used to accurately estimate the TMEn value of sorghum grain from its corresponding chemical composition.

This study was conducted to evaluate the effects of different levels of milk thistle meal on performance, blood biochemical indices, ileal bacterial counts and intestinal histology in laying hens fed diets containing different levels of metabolizable energy. A total number of 200 Leghorn laying hens (Hy-Line W-36) were randomly assigned to eight experimental treatments with five cage replicates of five birds each. Dietary treatments consisted of four levels of milk thistle meal (0%, 15%, 30% and 60%) and two levels of AME (11.09 and 12.34 MJ/kg) fed over a period of 80 days. In vitro studies revealed that the total phenolic component of milk thistle meal was 470.64 mg gallic acid equivalent/g of the sample, and its antioxidant activity for inhibiting the 2-2-diphenyl-1-picrichydrazyl free radical and reducing ferric ions was about 21% higher than that of butylated hydroxyltoluene (p < .05). Diets containing high level of AME led to improved egg production (p < .05), egg weight (p < .05), egg mass (p < .01) and feed conversion ratio (p < .01). In addition, offering diets containing high energy significantly enhanced (p < .01) serum triglyceride and malondialdehyde (MDA) concentrations as well as jejunal villus height. Dietary supplementation of 3% milk thistle meal resulted in the best feed conversion ratio (p < .05), reduction of ileal Escherichia coli enumeration (p < .01) and an enhancement in the villus height-to-crypt depth ratio (p < .05). Furthermore, feeding incremental levels of this meal led to remarkable decrease in serum cholesterol, triglyceride and MDA (p < .01) concentrations while significant increase in blood high-density lipoprotein content and goblet cell numbers (p < .05). The present findings indicate that milk thistle meal with high antioxidant and antibacterial properties in laying hen diets may improve health indices and productive performance.