Document Type : Research Paper
Author
Faculty member of Isfahan Research center for agricultural Science and Natural Resources
Abstract
Sorghum grain is an important ingredient in poultry diets. Nitrogen-corrected true metabolizable energy (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 of sorghum grain. Artificial neural networks are the powerful method which widely used in agriculture and poultry nutrition. Therefore In this study, an artificial neural network (ANN) and a multiple linear regression (MLR) models were used to predict the TMEn of sorghum grain based on its acid detergent fiber (ADF) and total phenols content. The accuracy of the models was calculated by R2, MS error and bias. The predictive ability of an ANN was compared with a MLR model using the same training data sets. The results of this study showed that it is possible to estimate sorghum grain TMEn with a simple analytical determination of ADF and phenolic content. The R2 values corresponding to testing and training of the ANN model showed a higher accuracy of prediction than that established by regression method (R2=84% vs 56% for training and R2=83% vs 47% for testig data sets respectively). In conclusion, the ANN model may be used to accurately estimate the TMEn value of sorghum grain from its corresponding chemical composition (ADF and total phenols content).
Keywords
Ahmadi, H., A. Golian, M. Mottaghitalab, and N. Nariman-Zadeh. (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. 87:1909–1912.
Ahmadi, H., and A. Golian. (2010). Growth analysis of chickens fed diets varying in the
percentage of metabolizable energy provided by protein, fat, and carbohydrate through artificial neural network. Poultry Science. 89:173-179.
AOAC. ( 2000). Official Methods of Analysis. Association of Official Analytical Chemists, Washington, DC,USA.
Bolzan, A. C., R. A. F. Machado, and J. C. Z. Piaia.(2008). Egg hatchability prediction by
multiple linear regression and artificial neural networks. Brazilian Journal of Poultry
Science. 10:97–102.
Boren, B., and R. D. Waniska. (1992). Sorghum seed color as an indicator of tannin content.
Journal of Applied Poultry Research. 1:117-121
Breuer, L.H., and C. K. Dohm. (1972). Comparative nutritive value of several sorghum
varietiesand hybrids. Journal of Agriculture and Food Chemistry. 20:83-86
Butler, L. G. (1990). The nature and amelioration of the antinutritional effects of tannins in
sorghum grain. Pages 191–205 In: Proceedings of the International Conference on
Sorghum Nutritional Quality, Feb 26–March 1, 1990
Chung, K. T., T. Y. Wong, C. I. Wei, Y. W. Huang, and Y. Lin. (1998). Tannins and human
health: A review. Critical Review of Food Science. 38:421–464.
Douglas, J. H., T. W. Sullivan, P. L. Bond, and F. J. Struwe. (1990). Nutrient composition
and metabolizable energy values of selected grain sorghum varieties and yellow corn.
Poultry Science. 69:1147-1155.
Dowling, L. F., C. Arndt, and B. R. Hamaker. (2002). Economic Viability of High
Digestibility Sorghum as Feed for Market Broilers. Agronomic Journal. 94:1050–1058.
Featherston, W. R., and J. C. Rogler. (1975). Influence of tannins on the utilization of
sorghum grain by rats and chicks. Nutrition Reports International. 11:491-497.
Flores, M. P., J. I. L. Castanon, and J. M. Mcnab. (1994). Effect of tannins on starch
digestibilityand TMEn of triticale and semi purified starches from triticale and field beans.
British Poultry Science. 35: 281-286.
Goering, H. K., and P. J. Van Soset. (1970). Forage fiber analyses (apparatus, Reagents T
Procedures and some Applications). Agriculture Handbook. NO. 379. ARS-USDA,
Washington, DC.
Griffiths, D. W. (1979). The inhibition of digestive enzymes by extracts of field beans
(Viciafaba). Journal of Science and Food Agriculture. 30: 458-462.
Griffiths, D. W., and G. Moseley. (1980). The effect of diets containing field beans of high
- or low polyphenolic content on the activity of digestive enzymes in the intestines of rats.
Journal of Agricultural and Food Chemistry. 31: 255-259
Hagerman, A. E., K. M. Reidl, G. A. Jones, K. N. Sovik, N. T. Ritchard, P. W. Hartzfield,
and T. L. Tiechel. (1998). High molecular weight plant polyphenolics (tannins) as
biological antioxidants. Journal of Agricultural and Food Chemistry. 46:1887–1892.
Halley, J. T., T. S. Nelson, L. K. Kirby, and J. O. York. (1986). Effect of tannin content of
Sorghum grain in poultry rations on dry matter digestion and energy utilization. Arkansas
Farm Research Agricultural Experiment Station, University of Arkansas, Fayetteville,
Arkansas 35 (2): 8
Haslam, E. (1981). Vegetable tannins. In: The Biochemistry of Plants, Vol. 7 (Ed Conn,
E.E.), Academic Press, New York, pp. 527-544
Hulan, H. W., and F. G. Proud foot. (1982). Nutritive value of sorghum grain for broiler
chickens. Canadian Journal of Animal Science. 62:869–875.
Jimenez-Ramsey, L. M., J. C. Rogler, T. L. Housley, L. G. Butler, and R. G. Elkin. (1994).
Absorption and distribution of 14C-labeled condensed tannin and related sorghum
phenolics in chickens. Journal of Agricultural and Food Chemistry. 42: 963-967.
Mahmood, S., and R. Smithard. (1993). A comparison of effects of body weight and feed
intake on digestion in broiler cockerels with effects of tannins. British Journal of
Nutrition. 70:701-709.
Moir, K. W., and J. K. Connor. (1977). A comparison of three fiber methods for predicting
the metabolizable energy content of sorghum grain for poultry. Animal Feed Science and
Technology. 2:197-203.
National Research Council, (1994). Nutrient Requirements for Poultry. 9th rev. ed. National
Academy Press, Washington, DC.
Nyachoti, C. M., J. L. Atkinson, and S. Leeson. (1997). Sorghum tannins: a review. World
Poultry Science Journal. 53:5-21.
Perai A. H., H. Nassiri Moghaddam , S. Asadpour, J. Bahrampour, and Gh. Mansoori.
(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.
89:1562–1568.
Roush, W. B., and T. Cravener. (1997). Artificial neural network prediction of amino acid
levels in feed ingredients. Poultry Science. 76:721–727
Roush, W. B., W. A. Dozier III, and S. L. Branton. (2006). Comparision of Gompertz and
neural networks models of broiler growth. Poultry Science. 85:794–797.
Roush, W.B., Y.K. Kirby, T.L. Cravener, and R.F Wideman. (1996). Artificial neural
networks prediction of ascites in broilers. Poultry Science. 75:1479-1487.
Salle, C. T., A. S. Guahyba, V. B. Wald, A. B. Silva, F. O. Salle, and V. P. Nascimento.
(2003). Use of artificial neural networks to estimate production variables of broilers
breeders in the production phase. British Poultry Science. 44:211-217.
Sibbald, I. R. (1976). A bioassay for true metabolizable energy of feedstuffs. Poultry
Science. 55:303–308.
Sibbald, I. R. (1977). The true metabolizable energy values of some feeding-stuffs. Poultry
Science. 56: 380-385.
STATSOFT. (2009). Statistica. (Data Analysis Software System). Version 8.0.Tulsa,OK:
Statistica Software Incorporation.
)