Determination of forage quality by near-infrared reflectance spectroscopy in soybean

Sovetgul Asekova, Sang Ik Han, Hong Jib Choi, Sang Jo Park, Dong Hyun Shin, Chan Ho Kwon, J. Grover Shannon, Jeong Dong Lee*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

23 Citations (Scopus)


Soybeans have been a favored livestock forage for centuries. However, only a few studies have been conducted to estimate the forage quality of soybean by near-infrared reflectance spectroscopy (NIRS). In this study, 353 forage soybean samples were used to develop near-infrared reflectance (NIR) equations to estimate four forage quality parameters: crude protein (CP), crude fat (CF), neutral detergent fiber (NDF), and acid detergent fiber (ADF). Samples included 181 recombinant inbred lines derived from PI 483463 (G. soja) × Hutcheson (G. max), 104 cultivated soybeans (G. max), and 68 wild soybeans (G. soja). Two NIR equations developed for CP and CF (2,5,5,1; multiple scatter correction [MSC]) and for NDF and ADF (1,4,4,1; MSC) were the best prediction equations for estimating these parameters. The coefficients of determination in the external validation set (r2) were 0.934 for CF, 0.909 for CP, 0.767 for NDF, and 0.748 for ADF. The relative predictive determinant ratios for MSC (2,5,5,1) calibration indicate that the CP (3.25) and CF (3.85) equations were acceptable for quantitative prediction of soybean forage quality, whereas the NDF (2.07) and ADF (1.97) equations were useful for screening purposes. The NIR calibration equations developed in this study will be useful in predicting soybean forage quality for these four quality parameters.

Original languageEnglish
Pages (from-to)45-52
Number of pages8
JournalTurk Tarim ve Ormancilik Dergisi/Turkish Journal of Agriculture and Forestry
Issue number1
Publication statusPublished - Jan 1 2016


  • Forage quality
  • Near infrared reflectance spectroscopy (NIRS)
  • Soybean
  • Wild soybean

ASJC Scopus subject areas

  • Forestry
  • Food Science
  • Ecology

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