Journal of The Korean Society of Grassland and Forage Science (한국초지조사료학회지)

The Korean Society of Grassland and Forage Science (한국초지조사료학회)
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Prediction of Chemical Composition and Fermentation Parameters in Forage Sorghum and Sudangrass Silage using Near Infrared Spectroscopy

  • Park, Hyung-Soo (Grassland & Forages Division, National Institute of Animal Science) ;
  • Lee, Sang-Hoon (Grassland & Forages Division, National Institute of Animal Science) ;
  • Choi, Ki-Choon (Grassland & Forages Division, National Institute of Animal Science) ;
  • Kim, Ji-Hye (Grassland & Forages Division, National Institute of Animal Science) ;
  • So, Min-Jeong (Grassland & Forages Division, National Institute of Animal Science) ;
  • Kim, Hyeon-Seop (Grassland & Forages Division, National Institute of Animal Science)
  • Received : 2015.09.05
  • Accepted : 2015.09.07
  • Published : 2015.10.01
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Abstract

This study was conducted to assess the potential of using NIRS to accurately determine the chemical composition and fermentation parameters in fresh coarse sorghum and sudangrass silage. Near Infrared Spectroscopy (NIRS) has been increasingly used as a rapid and accurate method to analyze the quality of cereals and dried animal forage. However, silage analysis by NIRS has a limitation in analyzing dried and ground samples in farm-scale applications because the fermentative products are lost during the drying process. Fresh coarse silage samples were scanned at 1 nm intervals over the wavelength range of 680~2500 nm, and the optical data were obtained as log 1/Reflectance (log 1/R). The spectral data were regressed, using partial least squares (PLS) multivariate analysis in conjunction with first and second order derivatization, with a scatter correction procedure (standard normal variate and detrend (SNV&D)) to reduce the effect of extraneous noise. The optimum calibrations were selected on the basis of minimizing the standard error of cross validation (SECV). The results of this study showed that NIRS predicted the chemical constituents with a high degree of accuracy (i.e. the correlation coefficient of cross validation ($R^2{_{cv}}$) ranged from 0.86~0.96), except for crude ash which had an $R^2{_{cv}}$ of 0.68. Comparison of the mathematical treatments for raw spectra showed that the second-order derivatization procedure produced the best result for all the treatments, except for neutral detergent fiber (NDF). The best mathematical treatment for moisture, acid detergent fiber (ADF), crude protein (CP) and pH was 2,16,16 respectively while the best mathematical treatment for crude ash, lactic acid and total acid was 2,8,8 respectively. The calibrations of fermentation products produced poorer calibrations (RPD < 2.5) with acetic and butyric acid. The pH, lactic acid and total acids were predicted with considerable accuracy at $R^2{_{cv}}$ 0.72~0.77. This study indicated that NIRS calibrations based on fresh coarse sorghum and sudangrass silage spectra have the capability of assessing the forage quality control

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