KOREAN JOURNAL OF CROP SCIENCE (한국작물학회지)

The Korean Society of Crop Science (한국작물학회)
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Estimating the Important Components in Three Different Sample Types of Soybean by Near Infrared Reflectance Spectroscopy

  • Lee, Ho-Sun (National Agrobiodiversity Center, National Academy of Agricultural Science, RDA) ;
  • Kim, Jung-Bong (Dep. of Agrofood Resources. National Academy of Agricultural Science, RDA) ;
  • Lee, Young-Yi (National Agrobiodiversity Center, National Academy of Agricultural Science, RDA) ;
  • Lee, Sok-Young (National Agrobiodiversity Center, National Academy of Agricultural Science, RDA) ;
  • Gwag, Jae-Gyun (National Agrobiodiversity Center, National Academy of Agricultural Science, RDA) ;
  • Baek, Hyung-Jin (National Agrobiodiversity Center, National Academy of Agricultural Science, RDA) ;
  • Kim, Chung-Kon (National Agrobiodiversity Center, National Academy of Agricultural Science, RDA) ;
  • Yoon, Mun-Sup (National Agrobiodiversity Center, National Academy of Agricultural Science, RDA)
  • Received : 2011.02.28
  • Published : 2011.03.30
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Abstract

This experiment was carried out to find suitable sample type for the more accurate prediction and non-destructive way in the application of near infrared reflectance spectroscopy (NIRS) technique for estimation the protein, total amino acids, and total isoflavone of soybean by comparing three different sample types, single seed, whole seeds, and milled seeds powder. The coefficient of determination in calibration ($R^2$) and coefficient of determination in cross-validation (1-VR) for three components analyzed using NIRS revealed that milled powder sample type yielded the highest, followed by single seed, and the whole seeds as the lowest. The coefficient of determination in calibration for single seed was moderately low($R^2$ 0.70-0.84), while the calibration equation developed with NIRS data scanned with whole seeds showed the lowest accuracy and reliability compared with other sample groups. The scatter plot for NIRS data versus the reference data of whole seeds showed the widest data cloud, in contrary with the milled powder type which showed flatter data cloud. By comparison of NIRS results for total isoflavone, total amino acids, and protein of soybean seeds with three sample types, the powder sample could be estimated for the most accurate prediction. However, based from the results, the use of single bean samples, without grinding the seeds and in consideration with NIRS application for more nondestructive and faster prediction, is proven to be a promising strategy for soybean component estimation using NIRS.

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References

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