Abstract
ABSTRACT: The 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.
Highlights
Corn (Zea mays L.) is considered food of well-defined chemical composition, with average values provided in food composition tables, factors such as soil fertility, genetic variety of cultivars, planting conditions, storage and processing can significantly alter the chemical composition of corn (Li et al, 2014)
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 of corn and validate mathematical models to predict the metabolizable energy values (ME) of corn for pigs using near infrared spectroscopy (NIRS)
The highest coefficients of determination were obtained for DM (R2 = 0.99), CP (R2 = 0.94), GE (R2 = 0.86) and EE (R2 = 0.83), with values for the standard error of cross-validation (SECV) of 0.39, 0.39, 19.62 and 0.24, for each parameter, respectively
Summary
Corn (Zea mays L.) is considered food of well-defined chemical composition, with average values provided in food composition tables, factors such as soil fertility, genetic variety of cultivars, planting conditions, storage and processing can significantly alter the chemical composition of corn (Li et al, 2014). Other methods have attempted to determine the chemical composition and energetic value of corn, such as the near infrared spectroscopy (NIRS) technology, which is rapid, non-destructive and analyzes more than one component at the same time, avoiding the use of chemical reagents and producing no waste (Swart et al, 2012). Some studies have indicated that NIRS is useful to predict the nutritive and energetic content of feed ingredients and diets for poultry (Valdes and Leeson, 1992, 1994), rabbits (Xiccato et al, 1999, 2003) and even swine (Aufrère et al, 1996; Van Barneveld et al, 1999). To the best of our knowledge there is just one report (Li et al, 2016) on the rapid prediction of DE and ME content in corn by NIRS in pigs
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