Prediction of Nutrient Composition and In-Vitro Dry Matter Digestibility of Corn Kernel Using Near Infrared Reflectance Spectroscopy

Abstract

Nutritive value analysis of feed is very important for the growth of livestock, and ensures the efficiency of feeds as well as economic status. However, general laboratory analyses require considerable time and high cost. Near-infrared reflectance spectroscopy (NIRS) is a spectroscopic technique used to analyze the nutritive values of seeds. It is very effective and less costly than the conventional method. The sample used in this study was a corn kernel and the partial least square regression method was used for evaluating nutrient composition, digestibility, and energy value based on the calibration equation. The evaluation methods employed were the coefficient of determination (R2) and the root mean squared error of prediction (RMSEP). The results showed the moisture content (R2val=0.97, RMSEP=0.109), crude protein content (R2val=0.94, RMSEP=0.212), neutral detergent fiber content (R2val=0.96, RMSEP=0.763), acid detergent fiber content (R2val=0.96, RMSEP=0.142), gross energy (R2val=0.82, RMSEP=23.249), in vitro dry matter digestibility (R2val=0.68, RMSEP=1.69), and metabolizable energy (approximately R2val >0.80). This study confirmed that the nutritive components of corn kernels can be predicted using near-infrared reflectance spectroscopy.