Prediction of the true digestible amino acid contents from the chemical composition of sorghum grain for poultry

Abstract

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.

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

“…Nowadays, digital camera systems and image analysis techniques are well established, robust, and relatively cheap and can form the basis of a real-time observation that computer image analysis technique is a suitable method for predicting tannin in sorghum grain both by regression and ANN models. A linear regression model was applied to all data (Table 1), and the following relationship was established: Tannin = 1.167 (SE= 0.141) -0.015 (SE= 0.003) × L* + 0.004 (SE=0.004) × a* -0.004 (SE=0.004) × b* R 2 = 0.88 Multiple linear regression (MLR) and artificial neural network (AAN) models were previously used to describe the correlation between chemical compositions and TMEn value of sorghum grain in poultry (Sedghi et al, 2011;Ebadi et al, 2011). The results of these studies showed that the ANN model may more accurately estimate TMEn of feed ingredients than those using the MLR model.…”

“…Tannin content in the pericarp is one of the most important factors affecting the feeding value of sorghum grain (Nyachoti, 1997;Selle et al, 2010) and adversely affects its metabolizable energy and protein utilization in poultry (Boren & Waniska, 1992;Black et al, 2005;Selle et al, 2010). An initial study on Iranian sorghum grain indicated that its true metabolizable energy corrected for nitrogen (TMEn) content ranged between 3003 and 3899 kcal/kg (Sedghi et al, 2011), and protein content between 10.9 and 13.8% (Ebadi et al, 2011) in high-and low-tannin sorghum, respectively. Tannin can reduce feed intake (Oduhu & Baker, 2005;Bryden et al, 2009b), metabolizable energy (Perez-Maldonado & Rodrigues, 2009;Bryden et al, 2009b;Sannamani et al, 2010;Sedghi et al, 2011) and amino acid digestibility (Selle et al, 2010;Ebadi et al, 2011) in broilers when present in sorghum.…”

“…An initial study on Iranian sorghum grain indicated that its true metabolizable energy corrected for nitrogen (TMEn) content ranged between 3003 and 3899 kcal/kg (Sedghi et al, 2011), and protein content between 10.9 and 13.8% (Ebadi et al, 2011) in high-and low-tannin sorghum, respectively. Tannin can reduce feed intake (Oduhu & Baker, 2005;Bryden et al, 2009b), metabolizable energy (Perez-Maldonado & Rodrigues, 2009;Bryden et al, 2009b;Sannamani et al, 2010;Sedghi et al, 2011) and amino acid digestibility (Selle et al, 2010;Ebadi et al, 2011) in broilers when present in sorghum. Therefore, performance can be reduced when broiler are fed diets containing sorghum (Donald et al, 2008;Oduhu & Baker, 2005).…”

Chemical Composition and Nutritive Value of South African Sorghum Varieties as Feed for Broiler Chickens

“…Amino acid digestibilities, AME and TME of the tannin varieties were low in the present study. Condensed tannins have been reported to reduce feed intake (Oduhu & Baker, 2005;Bryden et al, 2009), metabolizable energy (Bryden et al, 2009;Perez-Maldonado & Rodrigues, 2009;Sannamani et al, 2010;Sedghi et al, 2011) and amino acid digestibility (Selle et al, 2010;Ebadi et al, 2011) in broilers. Such limiting factors are the result of the poor nutritional quality of the protein (kafirins and glutelin) and subsequently attributed to low solubility and deficiencies in amino acids (Sastry et al, 1986;Selle et al, 2010).…”

Prediction of the total and standardized ileal digestible amino acid contents from the chemical composition of soybean meals of different origin in broilers

“…Therefore, nutritionists are highly interested in finding rapid, inexpensive, and accurate methods for assessing TAA content and concentration of SIDAA contents of feedstuffs. Several studies have shown that digestible AA of feedstuffs is correlated with its chemical composition ( Ebadi et al., 2005 , 2011 ; Soleimani Roudi et al., 2012 ). Previously, regression equations have been used to predict the TAA content in feed ingredients based on chemical composition ( NRC, 1994 ; Cravener and Roush, 1999 ).…”