Literature DB >> 24879691

Using a computer-controlled simulated digestion system to predict the energetic value of corn for ducks.

F Zhao1, L Zhang2, B M Mi2, H F Zhang2, S S Hou2, Z Y Zhang2.   

Abstract

Two experiments were conducted to develop a computer-controlled digestion system to simulate the digestion process of duck for predicting the concentration of ME and the metabolizability of gross energy (GE) in corn. In a calibration experiment, 30 corn-based calibration samples with a previously published ME concentration in 2008 were used to develop the prediction models for in vivo energetic values. The linear relationships were established between in vivo ME concentration and in vitro digestible energy (IVDE) concentration, and between in vivo metabolizability of GE (ME/GE) and in vitro digestibility of GE (IVDE/GE), respectively. In a validation experiment, 6 sources of corn with previously published ME concentration in 2008 randomly selected from the primary corn-growing regions of China were used to validate the prediction models established in the calibration experiment. The results showed that in calibration samples, the IVDE concentration was positively correlated with the AME (r = 0.9419), AMEn (r = 0.9480), TME (r = 0.9403), and TMEn concentration (r = 0.9473). Similarly, the IVDE/GE was positively correlated with the AME/GE (r = 0.95987), AMEn/GE (r = 0.9641), TME/GE (r = 0.9588), and TMEn/GE (r = 0.9637). The coefficient of determination greater than 0.88 and 0.91, and residual SD less than 45 kcal/kg of DM and 1.01% were observed in the prediction models for ME concentrations and ME/GE, respectively. Twenty-nine out of 30 calibration samples showed differences less than 100 kcal/kg of DM and 2.4% between determined and predicted values for 4 ME (AME, AMEn, TME, and TMEn) and for 4 ME/GE (AME/GE, AMEn/GE, TME/GE, and TMEn/GE), respectively. Using prediction models developed from 30 calibration samples, 6 validation samples further showed differences less than 100 kcal/kg of DM and 2% between determined and predicted values for ME and ME/GE, respectively. Therefore, the computer-controlled simulated digestion system can be used to predict the ME and ME/GE of corn for ducks with acceptable accuracy. Poultry Science Association Inc.

Entities:  

Keywords:  corn; duck; in vitro digestible energy; metabolizable energy; simulated digestion system

Mesh:

Year:  2014        PMID: 24879691     DOI: 10.3382/ps.2013-03532

Source DB:  PubMed          Journal:  Poult Sci        ISSN: 0032-5791            Impact factor:   3.352


  6 in total

1.  Effect of Corn Particle Size on the Particle Size of Intestinal Digesta or Feces and Nutrient Digestibility of Corn-Soybean Meal Diets for Growing Pigs.

Authors:  Qingtao Gao; Feng Zhao; Fangkun Dang; Hu Zhang; Ya Wang
Journal:  Animals (Basel)       Date:  2020-05-18       Impact factor: 2.752

2.  Research Note: The comparative study of energy utilization in feedstuffs for Muscovy ducks between in vivo and in vitro.

Authors:  H Wang; X F Zhang; S S Zhai; J J Yuan; W C Wang; Y W Zhu; L Yang
Journal:  Poult Sci       Date:  2020-11-28       Impact factor: 3.352

3.  Accuracy of predicting metabolizable energy from in vitro digestible energy determined with a computer-controlled simulated digestion system in feed ingredients for ducks.

Authors:  Yuming Wang; Liting Yin; Hu Zhang; Ke Li; Dailin Li; Feng Zhao
Journal:  Anim Nutr       Date:  2021-09-08

4.  Optimization of exogenous carbohydrases supplemented in broiler diets using in vitro simulated gastrointestinal digestion and response surface methodology.

Authors:  Yang Liu; Shengli Liu; Guitao Jiang; Qiuzhong Dai
Journal:  PLoS One       Date:  2021-11-15       Impact factor: 3.240

Review 5.  Methodological aspects of determining phosphorus digestibility in swine: A review.

Authors:  Yue She; Defa Li; Shuai Zhang
Journal:  Anim Nutr       Date:  2017-02-21

6.  The feasibility of enzyme hydrolysate gross energy for formulating duck feeds.

Authors:  J Wei; M Xie; J Tang; Y B Wu; Q Zhang; S S Hou
Journal:  Poult Sci       Date:  2020-04-25       Impact factor: 3.352

  6 in total

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