$\require{mediawiki-texvc}$

연합인증

연합인증 가입 기관의 연구자들은 소속기관의 인증정보(ID와 암호)를 이용해 다른 대학, 연구기관, 서비스 공급자의 다양한 온라인 자원과 연구 데이터를 이용할 수 있습니다.

이는 여행자가 자국에서 발행 받은 여권으로 세계 각국을 자유롭게 여행할 수 있는 것과 같습니다.

연합인증으로 이용이 가능한 서비스는 NTIS, DataON, Edison, Kafe, Webinar 등이 있습니다.

한번의 인증절차만으로 연합인증 가입 서비스에 추가 로그인 없이 이용이 가능합니다.

다만, 연합인증을 위해서는 최초 1회만 인증 절차가 필요합니다. (회원이 아닐 경우 회원 가입이 필요합니다.)

연합인증 절차는 다음과 같습니다.

최초이용시에는
ScienceON에 로그인 → 연합인증 서비스 접속 → 로그인 (본인 확인 또는 회원가입) → 서비스 이용

그 이후에는
ScienceON 로그인 → 연합인증 서비스 접속 → 서비스 이용

연합인증을 활용하시면 KISTI가 제공하는 다양한 서비스를 편리하게 이용하실 수 있습니다.

Effect of Sample Preparation on Prediction of Fermentation Quality of Maize Silages by Near Infrared Reflectance Spectroscopy

Asian-Australasian journal of animal sciences, v.18 no.5, 2005년, pp.643 - 648  

Park, H.S. (National Institute of Subtropical Agriculture, Rural Development Administration) ,  Lee, J.K. (Hanwoo Experiment Station, National Livestock Research Institute, RDA) ,  Fike, J.H. (Crop and Soil Environmental Science Department, Virginia Tech.) ,  Kim, D.A. (School of Agricultural Biotechnology, Seoul National University) ,  Ko, M.S. (National Institute of Subtropical Agriculture, Rural Development Administration) ,  Ha, Jong Kyu (School of Agricultural Biotechnology, Seoul National University)

Abstract AI-Helper 아이콘AI-Helper

Near infrared reflectance spectroscopy (NIRS) has become increasingly used as a rapid, accurate method of evaluating some chemical constituents in cereal grains and forages. If samples could be analyzed without drying and grinding, then sample preparation time and costs may be reduced. This study wa...

주제어

AI 본문요약
AI-Helper 아이콘 AI-Helper

* AI 자동 식별 결과로 적합하지 않은 문장이 있을 수 있으니, 이용에 유의하시기 바랍니다.

제안 방법

  • The first number indicates the order of derivative, the second number is the gap in nm over which the derivatives were calculated, the third number is the number of data points used in the first smoothing, and the fourth number refers to the number of nm over which the second smoothing was applied. Calibration statistics included the standard error of calibration (SEC), the coefficient of multidetermination in calibration (R2), and the standard error of cross-validation (SECV). Optimal calibrations were selected on the basis of minimizing the SECV.
  • These issues may be resolved in part by improvements in chemometrics software, spectral data transformation for scatter correction and partial least squares regression. Such advances have minimized some of the interference of particle size variation and water absorption presented by wet silage samples (Baker et al.
  • , 1998). Thus, this experiment was conducted to assess the effect of sample preparation (drying or liquid nitrogen treatment vs. fresh) methods on prediction of fermentation quality of corn silage, and to select an acceptable sample preparation method for wet silage.
본문요약 정보가 도움이 되었나요?

참고문헌 (20)

  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. 

  2. Baker, C. W., D. I. Givens and E. R. Deaville. 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. Anim. Feed Sci. Technol. 50:17-26. 

  3. Cozzolino, D. A. Fassio and A. Giminez. 2000. The use of nearinfrared reflectance spectroscopy (NIRS) to predict the composition of whole maize plants. J. Sci. Food Agric. 81:142-146. 

  4. Daniel Alomar, Rita Fuchslocher and Sergio Stockebrand. 1999. Effects of oven- or freeze-drying on chemical composition and NIR spectra of pasture silage. Anim. Feed Sci. Technol. 80:309-319. 

  5. De la Roza, B., A. Martinez, S. Modrono and B. Santos. 1996. Determination of the quality of fresh silage by near infrared reflectance spectroscopy. In (Ed. A. M. C. Davies and P. Williams), Near Infrared Spectroscopy: The Future Waves, Proceedings of the 7th International Conference on Near Infrared Spectroscopy, Montreal, Canada, 6-11 August 1995, NIR Publications, Chichester, UK. pp. 537-541. 

  6. 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. Nutrition Research Reviews. 10:83-114. 

  7. Gordon, F. J., K. M. Cooper, R. S. Park and R. W. J. Steen. 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. 

  8. Kennedy, C. A., J. A. Shelford and P. C. Williams. 1996. Near infrared spectroscopic analysis of intact grass silage and fresh grass for dry matter, crude protein and acid detergent fiber. In (Ed. A. M. C. Davies and P. Williams), Near Infrared Spectroscopy: The Future Waves, Proceedings of the 7th International Conference on Near Infrared Spectroscopy, Montreal, Canada, 6-11 August 1995, NIR Publications, Chichester, UK. pp. 524-530. 

  9. McDonald, P., A. R. Henderson and S. J. E. Heron. 1991. The biochemistry of silage, second edn. Chalcombe Publications, Marlow. p. 340. 

  10. Park, R. S., F. J. Gordon, R. E. Agnew and R. W. J. Steen. 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. 

  11. Park, R. S., R. E. Agnew and D. J. Kilpatrick. 2002. The effect of freezing and thawing on grass silage quality predictions based on near infrared reflectance spectroscopy. Anim. Feed Sci. Technol. 101:151-167. 

  12. Reeves, J. B., III, T. H. Blosser and V. F. Colenbrander. 1989. Near infrared reflectance spectroscopy for analyzing undried silage. J. Dairy Sci. 72:79-88. 

  13. 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. 

  14. Shenk, J. S. 1992. NIRS analysis of natural agricultural products. In (Ed. K. I. Hildrum, T. Isaaksson, T. Naes and A. Tandberg) Near Infrared Spectroscopy. Bridging the Gap between Data Analysis and NIR Applications. London: Ellis Horwood. pp. 235-240. 

  15. Shenk, J. S. and M. O. Westerhaus. 1995. The application of near infrared reflectance spectroscopy (NIRS) to forage analysis. In (Ed. G. C. Fahey, Jr.) Forage Quality, Evaluation, and Utilization. ASA, Madison. WI. pp. 406-449. 

  16. Sinnaeve, G., P. Dardenne, R. Agneessens and R. Biston. 1994. The use of near infrared spectroscopy for the analysis of fresh grass silage. J. Near Infrared Spectrosc. 2:79-84. 

  17. Watson, C. A., G. Etchevers and W. C. Shuey. 1976. Relationship between ash and protein contents of flourmill streams determined with the InfraAnalyzer and standard approved methods. Cereal Chem. 53:803-804. 

  18. Williams, P. C. 1987. Variables affecting near-infrared reflectance spectroscopic analysis. In (Ed. P. Williams and K. Norris) Near-Infrared Technology in the Agricultural and Food Industries. St. Paul, MN: American Association of Cereal Chemists Inc. pp. 143-167. 

  19. Windham, W. R. 1987. Influence of grind and gravimetric technique on dry matter determination of forages intended for analysis by near infrared reflectance spectroscopy. Crop Sci. 27:773-776. 

  20. Windham, W. R., J. A. Robertsons and R. G. Leffler. 1987. A comparison of methods for moisture determination of forages for near infrared reflectance spectroscopy calibration and validation. Crop Sci. 27:777-783. 

관련 콘텐츠

오픈액세스(OA) 유형

BRONZE

출판사/학술단체 등이 한시적으로 특별한 프로모션 또는 일정기간 경과 후 접근을 허용하여, 출판사/학술단체 등의 사이트에서 이용 가능한 논문

섹션별 컨텐츠 바로가기

AI-Helper ※ AI-Helper는 오픈소스 모델을 사용합니다.

AI-Helper 아이콘
AI-Helper
안녕하세요, AI-Helper입니다. 좌측 "선택된 텍스트"에서 텍스트를 선택하여 요약, 번역, 용어설명을 실행하세요.
※ AI-Helper는 부적절한 답변을 할 수 있습니다.

선택된 텍스트