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http://dx.doi.org/10.11109/JAES.2014.20.3.117

The Effect of Representative Dataset Selection on Prediction of Chemical Composition for Corn kernel by Near-Infrared Reflectance Spectroscopy  

Choi, Sung-Won (Korea Spectral Products)
Lee, Chang-Sug (Korea Spectral Products)
Park, Chang-Hee (Korea Spectral Products)
Kim, Dong-Hee (Korea Spectral Products)
Park, Sung-Kwon (National Institute of Animal Science, RDA)
Kim, Beob-Gyun (Department of Animal Science and Technology, Konkuk University)
Moon, Sang-Ho (Division of Food Bio Science, Konkuk University)
Publication Information
Journal of Animal Environmental Science / v.20, no.3, 2014 , pp. 117-124 More about this Journal
Abstract
The objectives were to assess the use of near-infrared reflectance spectroscopy (NIRS) as a tool for estimating nutrient compositions of corn kernel, and to apply an NIRS-based indium gallium arsenide array detector to the system for collecting spectra and analyzing calibration equations using equipments designed for field application. Partial Least Squares Regression (PLSR) was employed to develop calibration equations based on representative data sets. The kennard-stone algorithm was applied to induce a calibration set and a validation set. As a result, the method for structuring a calibration set significantly affected prediction accuracy. The prediction of chemical composition of corn kernel resulted in the following (kennard-stone algorithm: relative) moisture ($R^2=0.82$, RMSEP=0.183), crude protein ($R^2=0.80$, RMSEP=0.142), crude fat ($R^2=0.84$, RMSEP=0.098), crude fiber ($R^2=0.74$, RMSEP=0.098), and crude ash ($R^2=0.81$, RMSEP=0.048). Result of this experiment showed the potential of NIRS to predict the chemical composition of corn kernel.
Keywords
Chemical composition; Corn kernel; Kennard-Stone; NIRS; PLSR;
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