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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

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.

Keywords

References

  1. Berardo N., G. Mazzinelli, P. Valoti, P. Lagana, and R. Redaelli. 2009. Characterization of maize germplasm for the chemical composition of the grain. J. Agric. Food Chem. 2009, 57, 2378-2384. https://doi.org/10.1021/jf803688t
  2. Choung, M.G., I. Y. Baek, S.T. Kang, W.Y. Han, D.C. Shin, H.P. Moon, and K.H. Kang. 2001. Determination of protein and oil contents in soybean seed by near infrared reflectance spectroscopy. Korean J. Crop Sci. 46: 106-111.
  3. Font, R., M.D. Rio, J.M. Fernandez-Martinez, and A. de Haro-Bailon.2004. Use of near-infrared spectroscopy for screening the individual and total glucosinolate contents in Indian mustard seed (Brassica juncea L. Czern. & Coss.). Journal of Agricultural and Food Chemistry 52: 3563-3569. https://doi.org/10.1021/jf0307649
  4. Fontaine, J., J. Hrr, and B. Schirmer. 2001. Near-infrared reflectance spectroscopy enables the fast and accurate prediction of the essential amino acid contents in soy, rapeseed meal, sunflower meal, peas, fishmeal, meat meal products, and poultry meal. J. Agric. Food Chem. 49: 57-66. https://doi.org/10.1021/jf000946s
  5. Igne, B., G.R. Rippke, and C.R. Hurburgh. Measurement of whole soybean fatty acids by near infrared spectroscopy. 2008. J. of Ame. Oil Chemists' Society 85: 1105-1113. https://doi.org/10.1007/s11746-008-1311-1
  6. Jeong, J.C., H.C. Ok, O.S. Hurr, and C.G. Kim. 2008. Prediction of sprouting capacity using near-infrared spectroscopy in potato tubers. Am. J. Pot. Res. 89: 309-314.
  7. Kim, Y.H., H.K. Ahn, E.S. Lee, and H.D. Kim. 2008. Development of prediction model by NIRS for anthocyanin contents in black colored soybean. J. Crop. Sci. 53: 15-20.
  8. Kovalenko, I.V., G.R. Rippke, and C.R. Hurburgh. 2006. Measurement of soybean fatty acids by near-infrared spectroscopy: linear and nonlinear calibration methods. J. Am. Oil Chem. Soc. 83: 421-427. https://doi.org/10.1007/s11746-006-1221-z
  9. Lee, Y.Y., J.B. Kim, H.S. Lee, S.Y. Lee, J.G. Gwag, H.C Ko, Y.C. Huh, D.Y. Hyun, and C.K. Kim. 2010. Application of near0infrared reflectance spectroscopy (NIR) method to rapid determination of seed protein in coarse cereal germplasm. J. Crop Sci. 55: 357-364.
  10. McGlone, V.A., R.B. Jordan, R. Seelye, and P.J. Martinsen. 2002. Coparing density and NIR methods for measurement of Kiwifruit dry matter and soluble solids content. Postharv. Biol. Technol. 26: 191-198. https://doi.org/10.1016/S0925-5214(02)00014-5
  11. Min, T.G. and W.S. Kang. 2008. Nondestructive calssification between normal and artificially aged corn(Zea mays L.) seeds using near infrared spectroscopy. J. Crop Sci. 53: 314-319.
  12. Mohri, T. Y. Sakata and M. Otsuka. 2009. Quantative evaluation of glycyrrhizic acid that affects the product quality of kakkonto extract, a traditional herbal medicine, by a chemometric near infrared spectroscopic method. J. Near Infrared Spectrosc. 17: 84-100.
  13. Petisco, C., B. Garcia-Criado, B.R. Vazquez de Aldana, I. Zabalgogeazcoa, S. Mediavilla, and A. Garcia-Ciudad. 2005. Use of near-infrared reflectance spectroscopy in predicting nitrogen, phosphorus and calcium contents in heterogeneous woody plant species. Anal. Bioanal Chem. 382: 458-465. https://doi.org/10.1007/s00216-004-3046-7
  14. Sato, T., K. Eguchi, T. Hatano, and Y. Nishiba. Use of near-infrared reflectance spectroscopy for the estimation of the isoflavone contents of soybean seeds. 2008. Plant Prod. Sci. 11: 481-486. https://doi.org/10.1626/pps.11.481
  15. Schmilovitch, Z., A. Mizrach, A. Hoffman, H. Egozi, and Y. Fuchs. 2000. Determination of mango physiological indices by near-infrared spectrometry. Postharv. Biol. Technol. 19: 245-252. https://doi.org/10.1016/S0925-5214(00)00102-2
  16. Tkachuk, R., F.D. Kuzina, and R.D. Reichert. 1987. Analysis of protein in ground and whole field peas by near-infrared reflectance spectroscopy. Cereal Chem. 64: 418-422.
  17. Velasco, L., M. Christian, and C. Heiko. 1999. Estimation of seed weight, oil content and fatty acid composition in intact single seeds of rapeseed (Brassica napus L.) by near-infrared reflectance spectroscopy. Becker. Euphytica 106: 79-85. https://doi.org/10.1023/A:1003592115110
  18. Wong, H. and P.A. Murphy. 1994. Isoflavone content in commercial soybean food. J. Agric. Food Chem. 42: 1674-1677. https://doi.org/10.1021/jf00044a017