title: The impact of dietary protein source on observed and predicted metabolizable energy of dry extruded dog foods.
abstract: Fifty-five observations were used to determine the ME content of 8 foods containing different protein sources. The major protein sources tested included low-oligosaccharide whole soybeans; 2 low-oligosaccharide, low-phytate whole soybeans; 2 conventional soybean meals; low-ash poultry meal; low-oligosaccharide, low-phytate soybean meal; and conventional whole soybeans. The ME content of all foods ranged from 3,463 to 4,233 kcal/kg of DM. The first objective was to utilize the observed ME data and test the accuracy of the modified Atwater equation. In this study, the modified Atwater equation generally underpredicted ME compared with the observed ME (residual mean = 247 kcal/kg). The second objective was to use individual data to develop an equation, based on the chemical composition of the food, to predict the ME content of the foods. A multivariate regression analysis was used to predict ME content based on chemical composition. Five models were fitted to the data. Model 1 included CP, ether extract (EE), and crude fiber (CF). Because the foods varied in protein sources, and the ratio of total AA (TAA) to non-AA (NAA) CP ranged from 3.5:1 to 14.4:1, it was hypothesized that accounting for the proportion of TAA and NAA in CP would improve the fit of the model. Therefore, model 2 included TAA, NAA, EE, and CF. Defining CP in terms of TAA and NAA improved the r2 of the model from 0.46 to 0.79. Subsequently, models 3, 4, and 5 replaced the CF term with ADF, NDF, and hemicellulose (HEM). Model 3 included TAA, NAA, EE, and NDF. Model 4 included TAA, NAA, EE, ADF, and HEM. Model 5 included TAA, NAA, EE, and HEM. Defining dietary fiber in terms of HEM improved the r2 of model 2 from 0.79 to 0.81. Residual analysis suggested that replacing the CF term with HEM (model 5) improved the prediction of ME content. In contrast, defining fiber in terms of NDF (model 3) did not result in an improvement over model 2, whereas the ADF term (model 4) did not (P > 0.34) contribute to the overall model. Fractionating CP into TAA and NAA components further defined the chemical composition of the food. These data suggest that defining protein composition improves the accuracy of predicting the ME content of dog foods.
authors.affiliation: Department of Animal and Food Sciences, University of Kentucky, Lexington 40546, USA.
authors.forename: R M
authors.initials: RM
authors.lastname: Yamka
authors.forename: K R
authors.initials: KR
authors.lastname: McLeod
authors.forename: D L
authors.initials: DL
authors.lastname: Harmon
authors.forename: H C
authors.initials: HC
authors.lastname: Freetly
authors.forename: W D
authors.initials: WD
authors.lastname: Schoenherr
mesh_terms: D000821:Animal Feed / Q000032:analysis*; D000824:Animal Nutritional Physiological Phenomena; D000818:Animals; D004032:Diet / Q000662:veterinary; D004044:Dietary Proteins / Q000378:metabolism* / Q000494:pharmacology; D004285:Dogs; D004734:Energy Metabolism / Q000187:drug effects; D005260:Female; D009844:Oligosaccharides / Q000737:chemistry; D010833:Phytic Acid / Q000737:chemistry; D011202:Poultry Products; D013025:Glycine max
chemical_list: D004044:Dietary Proteins; D009844:Oligosaccharides; D010833:Phytic Acid