Predicting the nutritive value of high moisture grain corn by near infrared reflectance spectroscopy

Abstract

The aim of this study was to evaluate the potential use of near infrared reflectance (NIR) spectroscopy to predict the nutritive value of high moisture grain corn (HMC). Additionally the use of the jack-knifing as a method to reduce redundant wavelengths was explored when the calibration models were developed. The coefficient of determination in calibration ( R CAL 2 ) and the standard error in cross validation (SECV) were ( R CAL 2 = 0.90 , SECV: 2.6%) for dry matter, ( R CAL 2 = 0.85 , SECV: 0.52%) for crude protein, ( R CAL 2 = 0.90 , SECV: 1.8%) for acid detergent fibre, (ADF), ( R CAL 2 = 0.91 , SECV: 2.0%) for in vitro organic matter digestibility (OMD), ( R CAL 2 = 0.84 , SECV: 0.33%) for ash, ( R CAL 2 = 0.91 , SECV: 0.3%) for pH and ( R CAL 2 = 0.90 , SECV: 1.07%) for ammonia nitrogen (N), respectively. The results from this study suggested that dry matter, acid detergent fibre and in vitro organic matter digestibility were accurately predicted using NIR spectroscopy in HMC samples. The use of the jack-knifing method improved the calibration models obtained.