Advanced SearchSearch Tips
Prediction of Nutrient Composition and In-Vitro Dry Matter Digestibility of Corn Kernel Using Near Infrared Reflectance Spectroscopy
facebook(new window)  Pirnt(new window) E-mail(new window) Excel Download
 Title & Authors
Prediction of Nutrient Composition and In-Vitro Dry Matter Digestibility of Corn Kernel Using Near Infrared Reflectance Spectroscopy
Choi, Sung Won; Lee, Chang Sug; Park, Chang Hee; Kim, Dong Hee; Park, Sung Kwon; Kim, Beob Gyun; Moon, Sang Ho;
  PDF(new window)
Nutritive value analysis of feed is very important for the growth of livestock, and ensures the efficiency of feeds as well as economic status. However, general laboratory analyses require considerable time and high cost. Near-infrared reflectance spectroscopy (NIRS) is a spectroscopic technique used to analyze the nutritive values of seeds. It is very effective and less costly than the conventional method. The sample used in this study was a corn kernel and the partial least square regression method was used for evaluating nutrient composition, digestibility, and energy value based on the calibration equation. The evaluation methods employed were the coefficient of determination () and the root mean squared error of prediction (RMSEP). The results showed the moisture content (, RMSEP=0.109), crude protein content (, RMSEP=0.212), neutral detergent fiber content (, RMSEP=0.763), acid detergent fiber content (, RMSEP=0.142), gross energy (, RMSEP=23.249), in vitro dry matter digestibility (, RMSEP=1.69), and metabolizable energy (approximately >0.80). This study confirmed that the nutritive components of corn kernels can be predicted using near-infrared reflectance spectroscopy.
Nutrient Composition;Digestibility;Corn kernel;NIRS;PLSR;
 Cited by
Models for Estimating Yield of Italian Ryegrass in South Areas of Korean Peninsula and Jeju Island,;;;;

한국초지조사료학회지, 2016. vol.36. 3, pp.223-236 crossref(new window)
AOAC. 2005. Official methods of analysis (16th ed.), Association of Official Analytical Chemist, Arlington, VA. Washington D. C., USA.

Armstrong, P.R. 2006. Rapid Single-Kernel NIR Measurement of Grain and Oil-Seed Attributes. Applied Engineering in Agriculture. 22(5):767-772. crossref(new window)

Boisen, S. and Fernandez, J.A. 1997. Prediction of the total tract digestibility of energy in feed stuffs and pig diets by in vitro analyses. Animal Feed Science and Technology. 68:277-286. crossref(new window)

Chin, W.W. 1998. The Partial Least Squares Approach to Structural Equation Modeling. Modern Methods for Business Research. NJ: Lawrence Erlbaum Associates. Mahwah. pp. 295-336.

De Groot, P.J., Postma, G.J., Melssen, W.J. and Buydens, L.M.C. 1999. Selecting a representative training set for the classification of demolition waste using remote NIR sensing. Analytical chimica acta. 392:67-75. crossref(new window)

Deaville, E.R. and Flinn, P.C. 2000. Near infrared spectroscopy: an alternative approach for the estimation of forage quality and voluntary intake. Forage evaluation in ruminant nutrition. CAB International. UK. pp. 301-320.

Goering, H.K. and Van Soest, P.J. 1970. Forage fiber analysis. Agriculture Handbook. No. 379. ARS-USDA. Washington, D. C.

Harris, L.E. 1970. In vitro dry matter and organic matter digestion (Moor modification of Tilly and Terry Technique). Nutrition Research Techniques for Domestic and Wild Animals. 1: 5051-5053.

Jones, G.M., Wade, N.S., Baker, J.P. and Ranck, E.M. 1987. Use of near infrared reflectance spectroscopy in forage testing. Journal of Dairy Science. 70:1086-1091. crossref(new window)

Lee, H.W., Kim, J.D., Kim, W.H. and Lee, J.K. 2009. Prediction on the quality of forage crop by near infrared reflectance spectroscopy. Journal of The Korean Society of Grassland Science. 29(1):31-36. crossref(new window)

Martens, H. and Naes, T. 1990. Multivariate calibration. Journal of Chemometrics. 4(6):441. crossref(new window)

Martin, G.E., Bacino, C.B. and Papp, N.L. 1980. Hypothermiaelicited by the intracerebral microinjection of neurotensin. Peptides. 1: 333-339 crossref(new window)

Norris, K.H., Barnes, R.E.F., Moore, J.E. and Shenk, J.S. 1976. Predicting forages quality by infrared reflectance spectroscopy. Journal of Animal Science. 43:889-897.

Park, H.S., Lee, J.K. and Lee, H.W. 2004. Applications of Near Infrared Reflectance Spectroscopy (NIRS) in Forage Evaluation. Journal of The Korean Society of Grassland Science. 24(1): 81-90. crossref(new window)

Park, H.S., Lee, J.K., Lee, H.W., Hwang, K.J., Jung, H.Y. and Ko, M.S. 2006. Effect of sample preparations on prediction of chemical composition for corn silage by near infrared reflectance spectroscopy. Journal of The Korean Society of Grassland Science. 26(1):53-62. crossref(new window)

Ruano-Ramos, A., Garcia-Ciudad, A. and Garcia-Criado, B. 1999. Determination of nitrogen and ash content in total herbage and botanical components of grassland systems with near infrared spectroscopy. Journal of the Science of Food and Agriculture. 79:137-143. crossref(new window)

Smith, K.F., REED, K.F.M. and FOOT, J.Z. 1997. An assessment of the relative importance of specific traits for the genetic improvement of nutritive value in dairy pasture. Grass and Forage Science. 52:167-175. crossref(new window)

Snyman, M.A., Olivier, J.J., Cloete, J.A.N. and King, B.R. 1993. Produksienorme vir Afrinoskape in twee omgewings. Karoo Agric. 5(1):25-28.

Starr, C.A., Morgan, A.G., and Smith, D.B., 1981. An evaluation of near infrared reflectance analysis in some plant breeding programs. Journal of Agricultural Science. 97:107-115. crossref(new window)

Tilly, J.M.A. and Terry, R.A. 1963. A two-stage technique for in vitro digestion of forage crops. Journal of British Grassland Society. 18:401-411

Valdes, E.V., Young, L.G., McMillan, I. and Winch, J.E. 1985. Analysis of hay, haylage and corn silage samples by near infrared reflectance spectroscopy. Journal of Animal Science. 65(3):753-760.

Valdes, E.V., Hunter, R.B. and Pinter, L. 1987. Determination of quality parameters by near infrared reflectance spectroscopy in whole-plant corn silage. Canadian Journal of Plant Science, 67:747-754. crossref(new window)

Varmuza, K. and Filzmoser, P. 2009. Introduction to Mutivariate Statistical Analysis in Chemometrics. Taylor & Francis Group. USA. pp. 103-190.

Zhou, L.J., Zhang, L.Y., Zhang, En.X., Li, J.T., Yang, W.J. and Wang, Z.Y. 2012. Rapid determination of swine available energy and amino acids in corn distillers dried grains with solubled by near-infrared reflectance spectroscopy. Animal Feed Science and Technology. 175:198-202. crossref(new window)