SINGLE-KERNEL MAIZE ANALYSIS BY NEAR-INFRARED HYPERSPECTRAL IMAGING
The objectives of this research were: (1) to develop a technique for creating calibrations to predict the constituentconcentrations of single maize kernels from near-infrared (NIR) hyperspectral image data, and (2) to evaluate the feasibilityof an NIR hyperspectral imaging spectrometer as a tool for the quality analysis of single maize kernels. Single kernels of maizewere analyzed by hyperspectral transmittance in the range of 750 to 1090 nm. The transmittance data were standardized usingan opal glass transmission standard and converted to optical absorbance units. Partial least squares (PLS) regression andprincipal components regression (PCR) were used to develop predictive calibrations for moisture and oil content using thestandardized absorbance spectra. Standard normal variate, detrending, multiplicative scatter correction, wavelengthselection by genetic algorithm, and no preprocessing were compared for their effect on model predictive performance. Themoisture calibration achieved a best standard error of cross-validation (SECV) of 1.20%, with relative performancedeterminant (RPD) of 2.74. The best oil calibration achieved an SECV of 1.38%, with an RPD of only 1.45. The performanceand subsequent analysis of the oil calibration reveal the need for improved methods of single-seed reference analysis.