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

  • 제36권1호
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  • Pages.50-57
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  • 2016
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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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수 처리 방법이 근적외선분광법을 이용한 옥수수 사일리지의 화학적 조성분 및 발효품질의 예측 정확성에 미치는 영향

Mathematical Transformation Influencing Accuracy of Near Infrared Spectroscopy (NIRS) Calibrations for the Prediction of Chemical Composition and Fermentation Parameters in Corn Silage

  • Park, Hyung-Soo (Grassland & Forages Division, National Institute of Animal Science) ;
  • Kim, Ji-Hye (Grassland & Forages Division, National Institute of Animal Science) ;
  • Choi, Ki-Choon (Grassland & Forages Division, National Institute of Animal Science) ;
  • Kim, Hyeon-Seop (Grassland & Forages Division, National Institute of Animal Science)
  • 투고 : 2016.03.03
  • 심사 : 2016.03.14
  • 발행 : 2016.03.31
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초록

본 연구는 국내산 원물 옥수수 사일리지의 품질을 신속하게 분석 평가하기 위한 NIRS DB 구축과 원물시료의 분석 예측능력을 향상시키기 위한 근적외선 스펙트라의 적정 수 처리 방법을 구명하기 위하여 수행되었다. 옥수수 사일리지는 전국 사료작물 사일리지 품질경연대회에 출품된 시료와 2014년부터 2015년까지 전국 조사료 품질검사 시범사업에 참여한 조사료 생산경영체, 농축협 TMR회사 및 생산농가에서 407점을 수집하였다. 옥수수 사일리지의 품질평가를 위한 NIRS DB 구축을 위해 수집된 시료를 근적외선 분광기를 이용하여 스펙트라를 측정하고 측정된 시료는 실험실에서 화학적 분석을 실시하였다. 다양한 수 처리 방법에 따른 사료가치 및 발효품질의 예측정확성을 평가하기 위하여 원시 스펙트라를 미분처리하여 최적의 수처리 방법을 구명하였다. 옥수수 원물 사일리지의 수분함량 예측능력은 1차 미분처리(1, 16, 16)한 것으로 나타났으며 NDF와 ADF의 최적 수 처리는 두 성분 모두에서 2, 16, 16 처리가 예측 정확성이 가장 높게 나타났다. 조단백질 함량의 예측능력은 1차 미분처리(1, 4, 4)가 SECV 0.51과 $R^2{_{cv}}$ 0.72로 가장 우수한 예측능력을 나타내었다. 옥수수 사일리지의 발효산물인 산도(pH) 예측 정확성은 원물 스펙트라를 1차 미분처리(1, 8, 8)한 것으로 나타났으며 젖산과 초산의 예측능력은 2차 미분처리(2, 16, 16)에서 각각 SECV 0.81% 및 0.71%의 분석오차로 높게 나타났다.

This study was conducted to determine the effect of mathematical transformation on near infrared spectroscopy (NIRS) calibrations for the prediction of chemical composition and fermentation parameters in corn silage. Corn silage samples (n=407) were collected from cattle farms and feed companies in Korea between 2014 and 2015. Samples of silage were scanned at 1 nm intervals over the wavelength range of 680~2,500 nm. The optical data were recorded as log 1/Reflectance (log 1/R) and scanned in intact fresh condition. The spectral data were regressed against a range of chemical parameters using partial least squares (PLS) multivariate analysis in conjunction with several spectral math treatments to reduce the effect of extraneous noise. The optimum calibrations were selected based on the highest coefficients of determination in cross validation ($R^2{_{cv}}$) and the lowest standard error of cross validation (SECV). Results of this study revealed that the NIRS method could be used to predict chemical constituents accurately (correlation coefficient of cross validation, $R^2{_{cv}}$, ranging from 0.77 to 0.91). The best mathematical treatment for moisture and crude protein (CP) was first-order derivatives (1, 16, 16, and 1, 4, 4), whereas the best mathematical treatment for neutral detergent fiber (NDF) and acid detergent fiber (ADF) was 2, 16, 16. The calibration models for fermentation parameters had lower predictive accuracy than chemical constituents. However, pH and lactic acids were predicted with considerable accuracy ($R^2{_{cv}}$ 0.74 to 0.77). The best mathematical treatment for them was 1, 8, 8 and 2, 16, 16, respectively. Results of this experiment demonstrate that it is possible to use NIRS method to predict the chemical composition and fermentation quality of fresh corn silages as a routine analysis method for feeding value evaluation to give advice to farmers.

키워드

참고문헌

  1. Abrams, S.M., Shenk, J.S. and Harpster, H.W. 1988. Potential of near infrared reflectance spectroscopy for analysis of silage composition. Journal of Dairy Science. 71(7):1955-1959. https://doi.org/10.3168/jds.S0022-0302(88)79766-0
  2. Adesogan, A.T., Owen, E. and Givens, D.I. 1998. Prediction of the in vivo digestibility of whole crop wheat from in vitro digestibility, chemical composition, in situ rumen degradability, in vitro gas production and near infrared reflectance spectroscopy. Animal Feed Science Technology. 74:259-272. https://doi.org/10.1016/S0377-8401(98)00175-8
  3. AOAC, 1990. Association of Official Analytical Chemists, Official Methods of Analysis. 15th Edition. Washington, DC.
  4. Baker, C.W., Givens, D.I. and Deaville, E.R. 1994. Prediction of organic matter digestibility in vivo of grass silages by near infrared reflectance spectroscopy: Effect of calibration method, residual moisture and particle size. Animal Feed Science Technology. 50:17-26. https://doi.org/10.1016/0377-8401(94)90006-X
  5. Deaville and Flynn. 2000. Near infrared reflectance spectroscopy: An alternative approach to forage quality evaluation. In Givens et al. 2000. Forage evaluation in animal nutrition. Page 201. CABI, Wallingford.
  6. Fussel, R.J. and McCalley, D.V. 1987. Determination of volatile fatty acids(C2-C5) and lactic acid in silage by gas chromatography. Analyst. 112:1213-1216. https://doi.org/10.1039/AN9871201213
  7. Garcia-Cuidad, A., Garcia-Criado, B., Perez-Corona, M.E. Vazquez de Aldana, B.R. and Ruano-Ramos A.N. 1993. Application of near-infrared reflectance spectroscopy to chemical analysis of heterogeneous and botanically complex grassland samples. Journal of the Science of Food and Agriculture. 63:419-426. https://doi.org/10.1002/jsfa.2740630407
  8. Geladi, P., MacDougall, D. and Martens, H. 1985. Linearization and scatter-correction for near-infrared reflectance spectra of meat. Journal of Applied Spectroscopy. 39:491-500. https://doi.org/10.1366/0003702854248656
  9. Givens, D.I., De Boever, J.L. and Deaville, E.R. 1997. The principles, practices and some future applications of near infrared spectroscopy for predicting the nutritive value of foods for animals and humans. Nutrition Research Reviews. 10: 83-114. https://doi.org/10.1079/NRR19970006
  10. Goering, H.K. and Van Soest, P.J. 1970. Forage Fiber Analysis. Agric. Handb. 379. US Department of Agriculture, Washington, DC.
  11. Gordon, F.J., Cooper, K.M., Park, R.S. and Steen, R.W.J. 1998. The prediction of intake potential and organic matter digestibility of grass silages by near infrared spectroscopy analysis of undried samples. Animal Feed Science Technology. 70:339-351. https://doi.org/10.1016/S0377-8401(97)00087-4
  12. MAFRA. 2014. The complementary measure for increased production of forage. pp. 10-11.
  13. Park, H.S., Lee, S.H., Choi, K.C., Lim, Y.C., Kim, J.G., Jo, K.Y. and Choi, G.J. 2012. Evaluation of the quality of Italian ryegrass silages by near infrared spectroscopy. Journal of The Korean Society of Grassland and Forage Science. 32(3):30-308.
  14. Park, H.S., Lee, S.H., Lim, Y.C., Seo, S., Choi, K.C., Kim, J.G., Kim, J.G. and Choi, G.J. 2013. Prediction of the Chemical Composition of Fresh Whole Crop Barley Silages by Near Infrared Spectroscopy. Journal of The Korean Society of Grassland and Forage Science. 33(3):171-176. https://doi.org/10.5333/KGFS.2013.33.3.171
  15. Park, H.S., Lee, S.H., Choi, K.C., Kim, J.H., Lee, K.W. and Choi G.J. 2014. Prediction of the Chemical Composition and Fermentation Parameters of Winter Rye Silages by Near Infrared Spectroscopy. Journal of The Korean Society of Grassland and Forage Science. 34(3):209-213. https://doi.org/10.5333/KGFS.2014.34.3.209
  16. Park, H.S., Lee, S.H., Choi, K.C., Kim, J.H., So, M.J. and Kim, H.S. 2015. Prediction of Chemical Composition and Fermentation Parameters in Forage Sorghum and Sudangrass Silage Using Near Infrared Spectroscopy. Journal of The Korean Society of Grassland and Forage Science. 35(3):257-263. https://doi.org/10.5333/KGFS.2015.35.3.257
  17. Park, R.S., Agnew, R.E., Gordon, F.J. and Steen, R.W.J. 1998. The use of near infrared reflectance spectroscopy on undried samples of grass silage to predict chemical composition and digestibility parameters. Animal Feed Science and Technology. 72:155-167. https://doi.org/10.1016/S0377-8401(97)00175-2
  18. Reeves III J.B. and Blosser T.H. 1989. Near infrared reflectance spectroscopy for analyzing undried silages. Journal of Dairy Science. 72: 79-88. https://doi.org/10.3168/jds.S0022-0302(89)79082-2
  19. Reeves J.B. III. and Blosser, T.H. 1991. Near infrared spectroscopic analysis of undried silages as influenced by sample grind, presentation method, and spectral region. Journal of Dairy Science. 742:882-895.
  20. Shenk, J.S. and Westerhaus, M.O. 1991. Population definition, sample selection, and calibration procedures for near infrared reflectance spectroscopy. Crop Science. 31:469-474. https://doi.org/10.2135/cropsci1991.0011183X003100020049x
  21. Valdes, E.V., Hunter, R.B. and Pinter, L. 1987. Determination of quality parameters by near infrared reflectance spectroscopy in whole-plant corn silage. Canadian Jouranl of Plant Science. 67:747-754. https://doi.org/10.4141/cjps87-102
  22. Williams, P.C. 1987. Variables affecting near-infrared reflectance spectroscopic analysis. In P. Williams and K. Norris (eds.) Near-Infrared Technology in the Agricultural and Food Industries. St. Paul, MN: American Association of Cereal Chemists Inc., pp. 143-167.