Effect of Sample Preparation on Predicting Chemical Composition and Fermentation Parameters in Italian ryegrass Silages by Near Infrared Spectroscopy

시료 전처리 방법이 근적외선분광법을 이용한 이탈리안 라이그라스 사일리지의 화학적 조성분 및 발효품질 평가에 미치는 영향

  • 박형수 (농촌진흥청 국립축산과학원) ;
  • 이상훈 (농촌진흥청 국립축산과학원) ;
  • 최기춘 (농촌진흥청 국립축산과학원) ;
  • 임영철 (농촌진흥청 국립축산과학원) ;
  • 김종근 (농촌진흥청 국립축산과학원) ;
  • 서성 (농촌진흥청 국립축산과학원) ;
  • 조규채 (케이씨테크)
  • Received : 2012.08.29
  • Accepted : 2012.10.16
  • Published : 2012.12.30

Abstract

Near infrared reflectance spectroscopy (NIRS) has become increasingly used as a rapid, accurate method of evaluating some chemical constituents in cereal and dired animal forages. Analysis of forage quality by NIRS usually involves dry grinding samples. Costs might be reduced if samples could be analyzed without drying or grinding. The objective of this study was to investigate effect of sample preparations on prediction ability of chemical composition and fermentation parameter for Italian ryegrass silages by NIRS. A population of 147 Italian ryegrass silages representing a wide range in chemical parameters were used in this investigation. Samples were scanned at 1nm intervals over the wavelength range 680-2500 nm and the optical data recorded as log 1/Reflectance (log 1/R) and scanned in oven-dried grinding and fresh ungrinding condition. The spectral data were regressed against a range of chemical parameters using partial least squares (PLS) multivariate analysis in conjunction with four spectral math treatments to reduced the effect of extraneous noise. The optimum calibrations were selected on the basis of minimizing the standard error of cross validation (SECV) and maximizing the correlation coefficient of cross validation (${R^2}_{CV}$). The results of this study show that NIRS predicted the chemical parameters with high degree of accuracy in oven-dried grinding treatment except for moisture contents. Prediction accuracy of the moisture contents was better for fresh ungrinding treatment (SECV 1.37%, $R^2$ 0.96) than for oven-dried grinding treatments (SECV 4.31%, $R^2$ 0.68). Although the statistical indexes for accuracy of the prediction were the lower in fresh ungrinding treatment, fresh treatment may be acceptable when processing is costly or when some changes in component due to the processing are expected. Results of this experiment showed the possibility of NIRS method to predict the chemical composition and fermentation parameter of Italian ryegrass silages as routine analysis method in feeding value evaluation and for farmer advice.

본 연구는 조사료 품질평가에서 근적외선 분광법의 현장 이용성 확대를 위하여 시료 전처리 방법에 따른 이탈리안 라이그라스 사일리지의 사료가치 및 발효품질의 예측정확성을 평가하기 위하여 수행되었으며 검량식 개발을 위하여 이탈리안 라이그라스 사일리지를 전북지역에서 174점을 수집하였다. 시료 전처리 방법은 사일리지를 건조 후 분쇄하는 방법과 원물 (생) 시료를 건조 분쇄하지 않는 방법을 두었으며 각각의 시료는 근적외선 분광기를 이용하여 스펙트럼을 측정한 후 측정된 스펙트럼과 실험실 분석값간에 상관관계를 이용한 다변량회귀분석법을 통하여 검량식을 유도한 다음 각 성분별로 예측 정확성을 평가하였다. 시료 전처리 방법에 따른 이탈리안 라이그라스 사일리지의 수분함량의 예측 정확성은 건조 분쇄하지 않은 원물(생)시료를 그대로 측정하는 방법 (SECV 1.37%, $R^2$=0.96)이 건조 분쇄처리 방법 (SECV 4.31%, $R^2$=0.68) 보다 예측 정확성이 높게 나타났다. ADF와 NDF 함량의 예측 정확성은 건조 후 분쇄처리한 방법이 개발된 검량식을 상호검증 (SECV)한 결과 각각 0.72% ($R^2$=0.97)와 0.85% ($R^2$=0.94)로 높게 나타났으며 조회분함량 평가에 대한 검량식개발 결과는 건조분쇄하지 않은 원물(생) 시료 전처리 방법에서 가장 낮은 정확성 (SECV 1.17%, $R^2$=0.66)을 나타내었다. pH와 젖산함량은 건조 분쇄 전처리 방법에서 각각 0.48 ($R^2$=0.87)와 0.24% ($R^2$=0.87)로 우수한 결과를 나타내었다. 이상의 연구결과를 종합해보면 근적외선분광법을 이용한 시료 전처리 방법에 따른 이탈리안 라이그라스 사일리지의 사료가치 및 발효품질 평가에 대한 예측정확성은 수분함량을 제외하고는 건조 후 분쇄하는 시료 전처리 방법이 예측 정확성 측면에서는 우수한 것으로 나타났으나 시료 전처리가 필요치 않은 원물(생) 시료의 측정 방법도 매우 양호한 예측 정확성을 보임으로써 실제 근적외선분광법의 현장 활용측면에서는 매우 유용한 전처리 방법으로 판단되어진다.

Keywords

References

  1. ANKOM Technology. 2005a. Method for determining neutral detergent fiber. ANKOM Technology, Fairport, NY. http://www.ankom.com/09_procedures/procedures2.shtml. Accessed May 8, 2005.
  2. ANKOM Technology. 2005b. Method for determining acid detergent fiber. ANKOM Technology, Fairport, NY. http://www.ankom.com/09_procedures/proceduresl.shtml. Accessed May 8, 2005.
  3. AOAC. 1990. Official Methods of Analysis, 15th ed. Association of Official Analytical Chemists, Washington, DC.
  4. Baker, C. W., Givens, D. I. and Deaville, E. R. 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.
  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.
  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. J. Sci. Food Agric. 63:419-426.
  8. Geladi, P., MacDougall, D. and Martens, H. 1985. Linearization and scattercorrection for near-infrared reflectance spectra of meat. Appl. Spectrosc. 39:491-500.
  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. Nutr. Res. Rev. 10:83-114.
  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. Anim. Feed Sci. Technol. 70:339-351.
  12. Hruschka, W. R. 1987. Data analysis: wavelength selection methods. 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.. p. 35-55.
  13. Park, R. S., Agnew, R. E., Gordon, F. J. and Steen, R. W. J. 1998. The use of near infrared reflectance spectroscopy (NIRS) on undried samples of grass silage to predict chemical composition and digestibility parameters. Anim. Feed Sci. Technol. 72: 155-167.
  14. Reeves III, J. B. and Blosser, T. H. 1989. Near infrared reflectance spectroscopy for analyzing undried silage. J. Dairy Sci. 72: 79-88.
  15. Reeves, III, J. B. and Blosser, T. H. 1991. Near infrared spectroscopic organic matter digestibility in vivo of grass silage by near infrared reflectance spectroscopy: Effect of calibration method, residual analysis of undried silages as influenced by sample grind, presentation method, and spectral region. J. Dairy Sci. 74:882-895.
  16. Shenk, J. S. and Westerhaus, M. O. 1991. Population definition, sample selection, and calibration procedures for near infrared reflectance spectroscopy. Crop Sci. 31:469-474.
  17. Valdes, E. V., Hunter, R. B. and Pinter, L. 1987. Determination of quality parameters by near infrared reflectance spectroscopy in whole-plant corn silage. Can. J. Plant Sci. 67:747-754.
  18. 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. p. 143-167.
  19. Windham, W. R., Mertens, D. R. and Barton, F. E. 1989. Protocol for NIRS calibration: Sample selection and equation development and validation. In G.C. Marten et al. (ed.) Near Infrared Reflectance Spectroscopy (NIRS): Analysis of Forage Quality. USDA Agric. Handb. 643. US Gov. Print. Office, Washington, D.C. pp. 96-103.