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학제간연구 과학기술학 뇌인지과학 기술정책 기타 복합학

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논문 기본 정보

자료유형
학술저널
저자정보
Kim, Ji Hye (Grassland & Forages Division, National Institute of Animal Science, Rural Development Administration) Park, Hyung Soo (Grassland & Forages Division, National Institute of Animal Science, Rural Development Administration) Choi, Ki Choon (Grassland & Forages Division, National Institute of Animal Science, Rural Development Administration) Lee, Sang Hoon (Grassland & Forages Division, National Institute of Animal Science, Rural Development Administration) Lee, Ki-Won (Grassland & Forages Division, National Institute of Animal Science, Rural Development Administration)
저널정보
한국초지조사료학회 한국초지조사료학회지 한국초지조사료학회지 제37권 제4호
발행연도
2017.1
수록면
350 - 357 (8page)

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Near infrared spectroscopy (NIRS) is a rapid and accurate method for analyzing the quality of cereals, and dried animal forage. However, one limitation of this method is its inability to measure fermentation parameters in dried and ground samples because they are volatile, and therefore, respectively lost during the drying process. In order to overcome this limitation, in this study, fresh coarse haylage was used to test the potential of NIRS to accurately determine chemical composition and fermentation parameters. Fresh coarse Italian ryegrass haylage samples were scanned at 1 nm intervals over a wavelength range of 680 to 2500 nm, and optical data were recorded as log 1/reflectance. Spectral data, together with first- and second-order derivatives, were analyzed using partial least squares (PLS) multivariate regressions; scatter correction procedures (standard normal variate and detrend) were used in order to reduce the effect of extraneous noise. Optimum calibrations were selected based on their low standard error of cross validation (SECV) values. Further, ratio of performance deviation, obtained by dividing the standard deviation of reference values by SECV values, was used to evaluate the reliability of predictive models. Our results showed that the NIRS method can predict chemical constituents accurately (correlation coefficient of cross validation, $R_{cv}^2$, ranged from 0.76 to 0.97); the exception to this result was crude ash ($R_{cv}^2=0.49$ and RPD = 2.09). Comparison of mathematical treatments for raw spectra showed that second-order derivatives yielded better predictions than first-order derivatives. The best mathematical treatment for DM, ADF, and NDF, respectively was 2, 16, 16, whereas the best mathematical treatment for CP and crude ash, respectively was 2, 8, 8. The calibration models for fermentation parameters had low predictive accuracy for acetic, propionic, and butyric acids (RPD < 2.5). However, pH, and lactic and total acids were predicted with considerable accuracy ($R_{cv}^2$ 0.73 to 0.78; RPD values exceeded 2.5), and the best mathematical treatment for them was 1, 8, 8. Our findings show that, when fresh haylage is used, NIRS-based calibrations are reliable for the prediction of haylage characteristics, and therefore useful for the assessment of the forage quality.
#Italian ryegrass haylage #Near Infrared Spectroscopy #Calibration model #Chemical composition #Fermentation parameters

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참고문헌 (1)

참고문헌 신청
Abrams, S. M. / 1988 / Potential of near infrared reflectance spectroscopy for analysis of silage composition / Journal of Dairy Science 71(7):1955~1959 google schola

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