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

  • 제26권1호
  • /
  • Pages.45-52
  • /
  • 2006
  • /
  • 2287-5824(pISSN)
  • /
  • 2287-5832(eISSN)
한국초지조사료학회 (The Korean Society of Grassland and Forage Science)
권호 표지
DOI QR코드

DOI QR Code

근적외선분광법을 이용한 옥수수 사일리지의 소화율 및 에너지 평가

Prediction of the Digestibility and Energy Value of Corn Silage by Near Infrared Reflectance Spectroscopy

  • 발행 : 2006.03.01
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

본 시험의 목적은 옥수수 사일리지의 소화율 및 에너지가치를 신속하고 정확하게 평가하는 방법으로서 근적외선분광법(NIRS)의 이용성을 확대하고 동시에 더욱 정확한 검량식을 유도하기 위하여 수행되었다. 112점의 옥수수 사일리지 시료를 이용하여 근적외선분광기를 이용하여 스펙트럼을 수집하였다. 검량기법은 변형부분 최소자승회귀법(MPLS), 산란보정법은 SNV-D 또한 1,4,4,1 수처리 방법을 이용하여 검량식을 작성하였다. 옥수수 사일리지의 소화율 측정방법에 따른 근적외선분광법의 예측 능력은 IVDMD, IVTD 및 CDMD 함량에서 각각 $SEP=1.57% (R^2v=0.70),\;SEP=1.13%(R^2v=0.73)$ 및 $SECV=1.74%\;(R^2v=0.77)$로 나타났으며 에너지 가치를 예측하기 위한 검량식 작성 및 검증 결과는 TDN, NEL 및 ME 함량에서 각각 SECV=0.69% $(R^2v=0.85)$, SECV=0.02% (R2v=0.88) 및 SECV=0.02% $(R^2v=0.88)$로 비교적 양호한 결과를 나타냈다.

This study was carried out to explore the accuracy of Near Infrared Reflectance Spectroscopy (NIRS) fer the prediction of digestibility and energy value of corn silages. The spectral data were regressed against a range of digestibility and energy parameters using modified partial least squares(MPLS) multivariate analysis in conjunction with first and second order derivatization, with scatter correction procedure(SNV-Detrend) to reduce the effect of extraneous noise. Calibration models for NIRS measurements gave multivariate correlation coefficients of determination$(R^2)$ and standard errors of cross validation of 0.92(SECV 1.73), 0.91(SECV 1.13) and 0.93(SECV 1.74) for in vitro dry matter digestibility(IVDMD), in vitro true digestibility(IVTD), and cellulase dry matter digestibility(CDMD), respectively. The standard error of prediction(SEP) and the multiple correlation coefficient of validation$(R^2v)$ on the validation set(n=39) was used in comparing the prediction accuracy. The SEP value was 0.30(TDN), 0.01(NEL), and 0.01(ME). The relative ability of NIRS to predict digestibility and energy value was very good for CDMD, total digestible nutrients(TDN), net energy fer lactation(NEL) and metabolizable energy(ME). This paper shows the potential of NIRS to predict the digestibility and energy value of con silage as a routine method in feeding programmes and for giving advice to farmers.

키워드

참고문헌

  1. Adesogan, A.T., E. Owen, and D.I. Givens. 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. Anim. Feed Sci. Technol. 74:259-272 https://doi.org/10.1016/S0377-8401(98)00175-8
  2. Anonymous. 1981. Proceedings 41st Semiannual Meeting. Am. Feed Manufacturers Association. Lexington, Ky. pp. 16-17
  3. Baker, C.W., D.I. Givens, and E.R. Deaville. 1994. Prediction of organic matter digestibility in vivo of grass silage by near infrared reflectance spectroscopy: effect of calibration method, residual moisture and particle size. Anim. Feed Sci. Technol. 50:17-26 https://doi.org/10.1016/0377-8401(94)90006-X
  4. Berglund, I., K. Larsson, and W. Indberg. 1990. Estimation of metabolizable energy for ruminants by near infrared reflectance photometry using multivariate methods. J. Sci. Food Agric. 52:339-349 https://doi.org/10.1002/jsfa.2740520307
  5. Bertrand, D., M. Lila, V. Furtoss, P. Robert, and G. Downey. 1987. Application of principal component analysis to the prediction of lucerne forage protein content and in vitro dry matter digestibility by NIR spectroscopy. J. Sci. Food Agric. 41:299-307 https://doi.org/10.1002/jsfa.2740410402
  6. Coors J.G., K.A. Albrecht and E.J. Bures. 1997. Ear-fill effects on yield and quality of silage corn. Crop Sci. 37:243-247 https://doi.org/10.2135/cropsci1997.0011183X003700010043x
  7. De Boever, J.L., B.G. Cottyn, F. Buysse, F.W. Wainman and J.M. Vanacker. 1986. The use of an enzymatic technique to predict digestibility, metabolizable and net energy of compound feedstuffs for ruminants. Anim. Feed Sci. Technol. 14:203-214 https://doi.org/10.1016/0377-8401(86)90093-3
  8. De Boever, J.L., B.G. Cottyn, D.L. De Brabander, J.M. Vanacker, and C.V. Boucque. 1996. Prediction of the feeding value of grass silages by chemical parameters, in vitro digestibility and near infrared reflectance spectroscopy. Anim. Feed Sci. Technol. 60: 103-115 https://doi.org/10.1016/0377-8401(95)00914-0
  9. De Boever, J.L., B.G. Cottyn, D.L. De Brabander, J.M. Vanacker, and C.V. Boucque. 1997. Prediction of the feeding value of maize silages by chemical parameters, in vitro digestibility and NIRS. Anim. Feed Sci. Technol. 66:211-212 https://doi.org/10.1016/S0377-8401(96)01101-7
  10. Givens, D.I., C.W. Baker, A.R. Moss and A.H. Adamson. 1991. A comparison of near infrared reflectance spectroscopy with three in vitro techniques to predict the digestibility in vivo of untreated and ammonia-treated cereal straws. Anim. Feed Sci. Technol. 35:83-94 https://doi.org/10.1016/0377-8401(91)90101-W
  11. Givens, D.I., J.L. De Boever, and E.R. Deaville. 1997. The principles, practices and some future applications of near infrared spectroscopy for predicting the nutritive value of foods for animals and humans. Nutr. Res. Rev. 10:83-114 https://doi.org/10.1079/NRR19970006
  12. Goering H.K. and P.J. Van Soest. 1970. Forage fiber analysis (Apparatus, reagents, procedures, and some applications). Washington, DC: USDA-ARS Agric. Handb. 379
  13. Jacquemoud, S. and F. Baret. 1990. Prospect: A model of leaf optical properties spectra. Remote Sensing Environ. 34:75-91 https://doi.org/10.1016/0034-4257(90)90100-Z
  14. Jocelyne Aufrere, Dominique Graviou, C. Demarquilly, J.M. Perez and J. Andrieu. 1996. Near infrared reflectance spectroscopy to predict energy value of compound feeds for swine and ruminants. Anim. Feed Sci. Technol. 62:77-90 https://doi.org/10.1016/S0377-8401(96)00995-9
  15. Moore, J. E. 1970. Procedure for the two-stage in vitro digestion of forage. In L. E. Harrision(ed.) Nutrition research technique for domestic and wild animals. Utah State Univ., Logan, USA
  16. Norris, K.H., R.F. Barnes, J.E. Moore, and J.S. Shenk. 1976. Predicting forage quality by near infrared reflectance spectroscopy. J. Anim. Sci. 43: 889-897 https://doi.org/10.2527/jas1976.434889x
  17. O'Keeffe, M., G. Downey and J.C. Brogan. 1987. The use of near infrared reflectance spectroscopy for predicting the quality of grass silage. J. Sci. Food Agric. 38: 209-216 https://doi.org/10.1002/jsfa.2740380304
  18. Park, R.S., F.J. Gordon, R.E. Agnew, R.J. Barnes, and R.W.J. Steen. 1997. The use of near infrared reflectance spectroscopy on dried samples to predict biological parameters of grass silage. Anim. Feed Sci. Technol. 68: 235-246 https://doi.org/10.1016/S0377-8401(97)00055-2
  19. Penuelas, J. and I. Filella. 1998. Visible and near infrared reflectance techniques for diagnosing plant physiological status. Trends in Plant Science. 3:151-156 https://doi.org/10.1016/S1360-1385(98)01213-8
  20. Shenk, J.S., M.O. Westerhaus, and M.R. Hoover. 1976. Analysis of forages by infrared reflectance. J. Dairy Sci. 62:807-812 https://doi.org/10.3168/jds.S0022-0302(79)83330-5
  21. Shenk, J.S. 1992. NIRS analysis of natural agricultural products. In K.I. Hildrum, T. Isaaksson, T. Naes, and A. Tandberg (Eds.) Near Infra-red Spectroscopy. Bridging the gap between data analysis and NIR applications. London: Ellis Horwood. pp. 235-240
  22. Shenk, J.S. and M.O. Westerhaus. 1991. Population definition, sample selection, and calibration procedures for near infrared reflectance spectroscopy. Crop Sci. 31:469-474 https://doi.org/10.2135/cropsci1991.0011183X003100020049x
  23. Smith, K.F. and P.C. Flinn. 1991. Monitoring the performance of a broad-based calibration for measuring the nutritive value of two independent populations of pasture using near infrared reflectance (NIR) spectroscopy. Aust. J. Exp. Agric. 31:205-210 https://doi.org/10.1071/EA9910205
  24. Tilley, I.M.A. and R.A Terry. 1963. A two-stage technique for the in vitro digestion of forage crops. J. Bri. Grassl. Soc. 18:104-111 https://doi.org/10.1111/j.1365-2494.1963.tb00335.x
  25. Valdes, E.V., G.E. Jones, and G.J. Hoekstra. 1990. Effect of growing year and application of a multiyear calibration for predicting quality parameters by near infrared reflectance spectroscopy in wholeplant corn forage. Can. J. Plant Sci. 70:747-755 https://doi.org/10.4141/cjps90-092
  26. Valdes, E.V., R.B. Hunter, and L. Pinter. 1987. Determination of quality parameters by near infrared reflectance spectroscopy in whole-plant corn silage. Can. J. Plant Sci. 67: 747-754 https://doi.org/10.4141/cjps87-102
  27. 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: Am. Assoc. of Cereal Chemists Inc. pp. 143-167
  28. Williams, P.C. and H.M. Cordiero. 1985. Effect of calibration practice on correction of errors induced in near-infrared protein testing of hard red spring wheat by growing location and season. J. Agric. Sci.(Cambridge) 104:113-123 https://doi.org/10.1017/S0021859600043057

피인용 문헌

  1. Prediction on the Quality of Forage Crop Seeded in Spring by Near Infrared Reflectance Spectroscopy (NIRS) vol.31, pp.4, 2011, https://doi.org/10.5333/KGFS.2011.31.4.409
  2. Prediction of Chemical Composition in Distillers Dried Grain with Solubles and Corn Using Real-Time Near-Infrared Reflectance Spectroscopy vol.33, pp.3, 2013, https://doi.org/10.5333/KGFS.2013.33.3.177
  3. Studies on 5 Protein Fractions Prediction of Forage Legume Mixture by NIRS vol.34, pp.3, 2014, https://doi.org/10.5333/KGFS.2014.34.3.214