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Methods of prediction of the digestible energy content of dog foods from gross energy value, proximate analysis and digestive nutrient content
Author(s) -
Kendall Peter T.,
Holme David W.,
Smith Philip M.
Publication year - 1982
Publication title -
journal of the science of food and agriculture
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.782
H-Index - 142
eISSN - 1097-0010
pISSN - 0022-5142
DOI - 10.1002/jsfa.2740330903
Subject(s) - proximate , water content , energy density , nutrient , ingredient , linear regression , zoology , food science , regression analysis , chemistry , moisture , mathematics , pet food , biology , statistics , physics , geotechnical engineering , organic chemistry , theoretical physics , engineering
The data from 106 digestibility trials with dogs were analysed with the objective of developing a predictive relationship between in vivo digestible energy (DE) content of foods and analytical components. The foods were commercial formulations arranged into three categories (wet, intermediate‐moisture and dry) on the basis of water content and ingredient profile. Mean ( n =106) apparent crude protein, acid ether extract and nitrogen‐free extract digestibility coefficients were 0.81, 0.85 and 0.79, respectively. The use of modified Atwater factors to predict food metabolisable energy (ME) gave values 104, 99 and 109% of in‐vivo DE values for wet, intermediate‐moisture and dry foods and thus over‐estimated food energy value. Sixteen single function or multiple regression equations were generated predicting DE content of each dog food category from gross energy (GE), Atwater ME, proximate components and digestible nutrient content. The DE content of wet and intermediate‐moisture foods was estimated with high precision using the single function of GE. Coefficients of determination ( R 2 ) for significant ( P < 0.001) linear regression equations were 0.96 and 0.89, respectively. Other useful multiple regression equations were identified between in‐vivo DE and combinations of total and digestible proximate components ( R 2 > 0.80). Less reliable relationships were obtained for the prediction of DE content of dry dog foods, probably because estimates of fibre content were not included; greatest precision ( R 2 = 0.60) was obtained for the linear regression between in‐vivo determined DE and analysed GE content.