The Use Of Chemical Composition And Fourier Near Infrared Spectroscopy To Predict The Net Energy Value Of Cottonseed Meal For 0-3-week-old Yellow Plumage Broiler

This trial was to study the feasibility of establishing predictive models of NE by Fourier near infrared spectroscopy(NIRS) and chemical compositions on the basis of 25 cottonseed meal NE values measured by comparative slaughter experiment. Method:(1) NE was calculated as NE for maintenance (NEm) plus NE for deposition(NEp). The NEm were measured by regression method with 5 levels of feed intake (group ad libitum and groups restricted feeding by 20%,40%,60%,80%, respectively). NEp were measured by the method of substitution. A total of 382 fasting yellow plumage broilers at 7 day of age with an average body weight of 62.20±0.64g were randomly allotted into every level and treatment with 6 replications of 2 chickens. The experiment lasted 7 days. (2) 25 samples of cottonseed meal were divided into the calibration set 1 of 17 samples and validation set of 8 samples; calibration set 1 was divided into 5 copies and adjusted the moisture content to 5 moisture ranges (9～10%,10～11%,11～12%,12～13% and 13～14%) as the calibration set 2. NIRS calibration models of NE were established under natural condition and a larger moisture background, respectively. (3) Predictive equations for AME, CP, EE, CF, NDF, ADF, Ash with NE were derived from the methods of one-dimensional and multivariate linear regression. The results were as follows:(1)The R2cal and RMSEE of 2 models were 0.999/0.985 and 0.033/ 0.083 MJ/kg DM, the R2cv and RMSECV were 0.966/0.967 and 0.120/0.117 MJ/kg DM, the R2vai and RMSEP of 2 models were 0.966/0.967 and 0.120/0.117 MJ/kg DM, and the results of paired-samples t test of NIRS predictive values and determined values were not significant(P>0.05). (2) The R2 and the RSD of the best regression equations from chemical compositions combined with AME was 0.985 and 0.093 MJ/kg DM. These results indicated that:(1) The NIRS can establish predictive model of cottonseed meal NE on the basis of less samples by enlarging background moisture range. (2) The chemical compositions combined with AME can establish predictive model of cottonseed meal NE and the best regression equation is NE=2.655+0.530AME-3.366CP+9.287EE-2.715CF. (3) The predictive accuracy of M2 is similar to the best equation from chemical compositions combined with AME.