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

Park, Hyung Soo (National Institute of Animal Science, RDA)
Lee, Sang Hoon (National Institute of Animal Science, RDA)
Choi, Ki Choon (National Institute of Animal Science, RDA)
Lim, Young Chul (National Institute of Animal Science, RDA)
Kim, Jong Gun (National Institute of Animal Science, RDA)
Seo, Sung (National Institute of Animal Science, RDA)
Jo, Kyu Chea (KC Tech Co. LTD.)
Publication Information
Journal of Animal Environmental Science / v.18, no.3, 2012 , pp. 257-266 More about this Journal
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.
Keywords
Forage quality; NIRS; Sample preparation; Calibration; Prediction accuracy;
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