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

  • 제33권3호
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  • Pages.177-184
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  • 2013
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  • 2287-5824(pISSN)
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  • 2287-5832(eISSN)
한국초지조사료학회 (The Korean Society of Grassland and Forage Science)
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Prediction of Chemical Composition in Distillers Dried Grain with Solubles and Corn Using Real-Time Near-Infrared Reflectance Spectroscopy

  • 투고 : 2013.08.07
  • 심사 : 2013.09.10
  • 발행 : 2013.09.30
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초록

This work was conducted to assess the use of Near-infrared reflectance spectroscopy (NIRS) as a technique to analyze nutritional constituents of Distillers dried grain with solubles (DDGS) and corn quickly and accurately, and to apply an NIRS-based indium gallium arsenide array detector, rather than a NIRS-based scanning system, to collect spectra and induce and analyze calibration equations using equipment which is better suited to field application. As a technique to induce calibration equations, Partial Least Squares (PLS) was used, and for better accuracy, various mathematical transformations were applied. A multivariate outlier detection method was applied to induce calibration equations, and, as a result, the way of structuring a calibration set significantly affected prediction accuracy. The prediction of nutritional constituents of distillers dried grains with solubles resulted in the following: moisture ($R^2$=0.80), crude protein ($R^2$=0.71), crude fat ($R^2$=0.80), crude fiber ($R^2$=0.32), and crude ash ($R^2$=0.72). All constituents except crude fiber showed good results. The prediction of nutritional constituents of corn resulted in the following: moisture ($R^2$=0.79), crude protein ($R^2$=0.61), crude fat ($R^2$=0.79), crude fiber ($R^2$=0.63), and crude ash ($R^2$=0.75). Therefore, all constituents except for crude fat and crude fiber were predicted for their chemical composition of DDGS and corn through Near-infrared reflectance spectroscopy.

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참고문헌

  1. AOAC. 1995. Official methods of analysis (16th ed.) Association of Official Analytical Chemist, Washington DC.
  2. Cowe, I.A. and McNicol, J.W. 1985. The use of principal components in the analysis of near infrared spectra. Applied Spectroscopy. 39:257-266. https://doi.org/10.1366/0003702854248944
  3. Deaville, E.R. and Flinn, P.C. 2000. Near-infrared spectroscopy: an Alternative approach for the estimation of forage quality and voluntary intake. CAB International 2000. Forage Evaluation in Ruminant Nutrition. UK. pp. 301-320.
  4. Filzmoser, P. 2004. A multivariate outlier detection method, in Proc. of the Seventh International Conference on Computer Data Analysis and Modeling. Minsk. Belarus. pp. 18-22.
  5. Jocelyne, A., Graviou, D., Demarquilly, C., Perez, J.M. and Andrieu, J. 1996. Near-infrared reflectance spectroscopy to predict energy value of compound feeds for swine and ruminants. Animal Feed Science and Technology. 62:77-90. https://doi.org/10.1016/S0377-8401(96)00995-9
  6. Kurt, V. and Filzmoser, P. 2009. Introduction to Mutivariate Statistical Analysis in Chemometrics (1st ed.). CRC Press. pp. 103-190.
  7. Kays, S.E. and Franklin, E.B. 2002. Rapid prediction of gross energy and utilizable energy in cereal food products using near-infrared reflectance spectroscopy. Journal of Agricultural and Food Chemistry. 50:1284-1289. https://doi.org/10.1021/jf011385n
  8. Kays, S.E., Douglas, D.A. and Sohn, M.Y. 2005. Prediction of fat in intact cereal food products using near-infrared reflectance spectroscopy. Journal of the science of food and agriculture. 85:1596-1602. https://doi.org/10.1002/jsfa.2157
  9. Marten, G.C., Shenk, J.S. and Barton, F.E. 1989. Near infrared reflectance analysis of forage quality. USDA Agriculture Handbook. No 643, US Government. Washington DC, USA.
  10. Marco, R., Atkinson, A. C. and Cerioli, A. 2009. Finding an unknown number of multivariate outliers. Journal of the Royal Statistical Society. B. 71(2):447-466. https://doi.org/10.1111/j.1467-9868.2008.00692.x
  11. Norris, K.H., Barnes, R.E.F., Moore, J.E. and Shenk, J.S. 1976. Predicting forages quality by infrared reflectance spectroscopy. Journal of Animal Science. 43:889-897.
  12. Park, H.S., Lee, J.K., Lee, H.W., Kim, S.G. and Ha, J.K. 2006. Prediction of the digestibility and energy value of corn silage by Near Infrared Reflectance Spectroscopy. Journal of the Korean Society of Grassland and Forage Science. 26(1):45-52. https://doi.org/10.5333/KGFS.2006.26.1.045
  13. Paul, G. 2006. Practical Guide to Chemometrics. 2nd ed, CRC Press. London. pp. 168-211.
  14. De Maesschalck, R., Jouan-Rimbaud, D. and Massart, D.L. 2000. The Mahalanobis distance. Chemometrics and Intelligent Laboratory Systems. 50:1-18. https://doi.org/10.1016/S0169-7439(99)00047-7
  15. Shenk, J.S. and Westerhaus, M.O. 1991. Population definition, sample selection, and calibration procedures for near infrared reflectance spectroscopy. Crop Science. 31(2):469-474. https://doi.org/10.2135/cropsci1991.0011183X003100020049x
  16. Westad, F. and Martens, H. 2000. Variable selection in near infrared spectroscopy based on significance testing in partial least squares regression. Journal of Near Infrared Spectroscopy. 8:117-124. https://doi.org/10.1255/jnirs.271