Using near infrared spectroscopy to predict metabolizable energy of corn for pigs
Keywords: chemical composition, prediction equations, validation, swine
AbstractThe chemical composition of corn is variable and the knowledge of its chemical and energetic composition is required for an accurate formulation of the diet. This study aimed to determine the chemical composition, that is, dry matter (DM), mineral matter (MM), neutral detergent fiber (NDF), acid detergent fiber (ADF), ether extract (EE), crude protein (CP), gross energy (GE) and energetic values of different varieties (batches) of corn and validate mathematical models to predict the metabolizable energy values (ME) of corn for pigs using near infrared spectroscopy (NIRS). Corn samples were scanned in the spectrum range between 1,100 and 2,500 nm, the model parameters were estimated by the modified partial least squares (MPLS) method. Ten prediction equations were inserted into the NIRS and used to estimate the ME values. The first degree linear regression models of the estimated ME values in function of the observed ME values were adjusted. The existence of a linear ratio was evaluated by detecting the significance to posterior estimates of the straight line parameters. The values of digestible energy and ME ranged from 3,400 to 3,752 and 3,244 to 3,611 kcal kg−1, respectively. The prediction equations, ME1 = 4334 – 8.1MM + 4.1EE – 3.7NDF; ME2 = 4,194 – 9.2MM + 1.0CP + 4.1EE – 3.5NDF; and ME7 = 16.13 – 9.5NDF + 16EE + (23CP × NDF) – (138MM × NDF) were the most adequate to predict the ME values of corn by using NIRS.
Download data is not yet available.
How to Cite
Ferreira, S., Vasconcellos, R., Rossi, R., Paula, V., Fachinello, M., Huepa, L., & Pozza, P. (2018). Using near infrared spectroscopy to predict metabolizable energy of corn for pigs. Scientia Agricola, 75(6), 486-493. https://doi.org/10.1590/1678-992x-2016-0509
Animal Science and Pastures
Copyright (c) 2018 Scientia Agricola
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.All content of the journal, except where identified, is licensed under a Creative Common attribution-type BY-NC.