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