Skip to main content Accessibility help
×
Home

Prediction of digestible amino acid and true metabolizable energy contents of sorghum grain from total essential amino acids

  • M. SEDGHI (a1), M. R. EBADI (a2), A. GOLIAN (a1) and H. AHMADI (a1)

Summary

Accurate information on metabolizable energy and true digestible amino acid (TDAA) content of sorghum grain is important in order to formulate sorghum-based poultry diets accurately. Estimates of ingredient nutritional values using bioassay methods require live birds and special facilities, which are time-consuming and costly. Accordingly, prediction by mathematical models would be of some considerable benefit. Sixty-eight samples of sorghum grain, representing 32 different varieties, were used to test the correlation between TDAA and nitrogen-corrected true metabolizable energy (TMEn) with total essential amino acids. Two methods of multiple linear regressions (MLR) and artificial neural network (ANN) models were used to find the relationship between total amino acids (model inputs) with TDAA and TMEn contents (model outputs) in sorghum grain. The fitness of the models was tested using R2, mean square (MS) error and bias. There is a strong relationship between total amino acid concentration with both TDAA and TMEn content in sorghum grain. The TDAA and TMEn values were more accurately estimated by ANN model compared to values obtained from the MLR model. The R2 values corresponding to testing and training of the ANN model showed a higher accuracy of prediction than the equation constructed by MLR method. Based on the experimental evidence, it is concluded that the TDAA and TMEn values in sorghum grain can be predicted from total essential amino acids using ANN models. Consequently, this method provides an opportunity to reduce the risk of formulating an unbalanced TDAA diet for poultry.

  • View HTML
    • Send article to Kindle

      To send this article to your Kindle, first ensure no-reply@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about sending to your Kindle. Find out more about sending to your Kindle.

      Note you can select to send to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be sent to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

      Find out more about the Kindle Personal Document Service.

      Prediction of digestible amino acid and true metabolizable energy contents of sorghum grain from total essential amino acids
      Available formats
      ×

      Send article to Dropbox

      To send this article to your Dropbox account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your <service> account. Find out more about sending content to Dropbox.

      Prediction of digestible amino acid and true metabolizable energy contents of sorghum grain from total essential amino acids
      Available formats
      ×

      Send article to Google Drive

      To send this article to your Google Drive account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your <service> account. Find out more about sending content to Google Drive.

      Prediction of digestible amino acid and true metabolizable energy contents of sorghum grain from total essential amino acids
      Available formats
      ×

Copyright

Corresponding author

*To whom all correspondence should be addressed. Email: mohamad_sedghi1@yahoo.com

References

Hide All
Ahmadi, H. & Golian, A. (2010). The integration of broiler chicken threonine responses data into neural network models. Poultry Science 89, 25352541.
Ahmadi, H., Golian, A., Mottaghitalab, M. & Nariman-Zadeh, N. (2008). Prediction model for true metabolizable energy of feather meal and poultry offal meal using group method of data handling-type neural network. Poultry Science 87, 19091912.
Andrews, R. P. & Baldar, N. A. (1985). Amino acid analysis of feed constituents. Science Tools 32, 4448.
Bryden, W. L. & Li, X. (2010). Amino acid digestibility and poultry feed formulation: expression, limitations and application. Revista Brasileira de Zootecnia (Brazilian Journal of Animal Science) 39, 279287.
Cravener, T. L. & Roush, W. B. (1999). Improving neural network prediction of amino acid levels in feed ingredients. Poultry Science 78, 983991.
Cravener, T. L. & Roush, W. B. (2001). Prediction of amino acid profiles in feed ingredients: genetic algorithm calibration of artificial neural networks. Animal Feed Science and Technology 90, 131141.
Duodu, K. G., Taylor, J. R. N., Belton, P. S. & Hamaker, B. R. (2003). Factors affecting sorghum protein digestibility. Journal of Cereal Science 38, 117131.
Ebadi, M. R., Pourreza, J., Jamalian, J., Edriss, M. A., Samie, A. H. & Mirhadi, S. A. (2005). Amino acid content and availability in low, medium and high tannin sorghum grain for poultry. International Journal of Poultry Science 4, 2731.
Elkin, R. G., Freed, M. B., Hamaker, B. R., Zhang, Y. & Parsons, C. M. (1996). Condensed tannins are only partially responsible for variations in nutrient digestibilities of sorghum grain cultivars. Journal of Agricultural and Food Chemistry 44, 848853.
Green, S., Bertrand, S. L., Duron, M. J. C. & Maillard, R. (1987). Digestibilities of amino acids in maize, wheat and barley meals, determined with intact and caecectomised cockerels. British Poultry Science 28, 631641.
Leeson, S. & Summers, J. D. (2005). Commercial Poultry Nutrition. 3rd edn. Nottingham, UK: Nottingham University Press.
Lou, W. & Nakai, S. (2001). Artificial neural network-based predictive model for bacterial growth in a simulated medium of modified-atmosphere-packed cooked meat products. Journal of Agricultural and Food Chemistry 49, 17991804.
Maiorka, A., Dahlke, F., Santin, E., Kessler, A. M. & Penz, A. M. Jr. (2004). Effect of energy levels of diets formulated on total or digestible amino acids basis on broiler performance. Revista Brasileira de Ciência Avícola (Brazilian Journal of Poultry Science) 6, 8791.
McNab, J. M. & Blair, J. C. (1988). Modified assay for true and apparent metabolizable energy based on tube feeding. British Poultry Science 29, 697707.
Moore, S. (1963). On the determination of cystine as cysteic acid. Journal of Biological Chemistry 238, 235237.
Neucere, N. J. & Sumrell, G. (1980). Chemical composition of different varieties of grain sorghum. Journal of Agricultural and Food Chemistry 28, 1921.
Parsons, C. M. (1985). Influence of caecectomy on digestibility of amino acids by roosters fed distillers’ dried grains with solubles. Journal of Agricultural Science, Cambridge 104, 469472.
Penkov, D., Pavlov, D. & Mihovsky, T. (2003). Comparative study of the aminoacid's true digestibility of different clover (Trifolium) varieties in experiments with ganders. Journal of Central European Agriculture 4, 191198.
Perai, A. H., Nassiri Moghaddam, H., Asadpour, S., Bahrampour, J. & Mansoori, Gh. (2010). A comparison of artificial neural networks with other statistical approaches for the prediction of true metabolizable energy of meat and bone meal. Poultry Science 89, 15621568.
Ravindran, V. & Bryden, W. L. (1999). Amino acid availability in poultry – in vitro and in vivo measurements. Australian Journal of Agricultural Research 50, 889908.
Roush, W. B. & Cravener, T. L. (1997). Artificial neural network prediction of amino acid levels in feed ingredients. Poultry Science 76, 721727.
Roush, W. B., Dozier Iii, W. A. & Branton, S. L. (2006). Comparison of Gompertz and neural network models of broiler growth. Poultry Science 85, 794797.
Salinas, I., Pro, A., Salinas, Y., Sosa, E., Becerril, C. M., Cuca, M., Cervantes, M. & Gallegos, J. (2006). Compositional variation amongst sorghum hybrids: effect of kafirin concentration on metabolizable energy. Journal of Cereal Science 44, 342346.
SAS Institute (2003). SAS/STAT Software Version 9. Cary, NC: SAS Institute Inc.
Sedghi, M., Ebadi, M. R., Golian, A. & Ahmadi, H. (2011). Estimation and modeling true metabolizable energy of sorghum grain for poultry. Poultry Science 90, 11381143.
Selle, P. H., Cadogan, D. J., Li, X. & Bryden, W. L. (2010). Implications of sorghum in broiler chicken nutrition. Animal Feed Science and Technology 156, 5774.
Sibbald, I. R. (1986). The T.M.E. System of Feed Evaluation: Methodology, Feed Composition Data and Bibliography. Technical Bulletin 4E. Ottawa, ON, Canada: Agriculture Canada.
STATSOFT (2009). Statistica (Data Analysis Software System). Version 8.0. Tulsa, OK: Statistica Software Incorporation.
Villamide, M. J. & San Juan, L. D. (1998). Effect of chemical composition of sunflower seed meal on its true metabolizable energy and amino acid digestibility. Poultry Science 77, 18841892.
Wong, J. H., Lau, T., Cai, N., Singh, J., Pedersen, J. F., Vensel, W. H., Hurkman, W. J., Wilson, J. D., Lemaux, P. G. & Buchanan, B. B. (2009). Digestibility of protein and starch from sorghum (Sorghum bicolor) is linked to biochemical and structural features of grain endosperm. Journal of Cereal Science 49, 7382.

Metrics

Full text views

Total number of HTML views: 0
Total number of PDF views: 0 *
Loading metrics...

Abstract views

Total abstract views: 0 *
Loading metrics...

* Views captured on Cambridge Core between <date>. This data will be updated every 24 hours.

Usage data cannot currently be displayed