When corn is processed in a conventional dry-grind ethanol process, a portion of the corn starch is not readily converted into ethanol. The amount of unconverted, or unreacted, starch varies according to several factors including storage time and processing conditions. The current method in determining the amount of unreacted starch is based on an enzyme assay which is time-consuming and does not lend itself for inline measurements of corn in the processing plants. A rapid method for determining the unreacted starch in corn would be advantageous so that the mix of enzymes and processing conditions can be adjusted to ensure maximum ethanol yields. In this study, we demonstrate the feasibility of using Fourier transform near infrared (FT-NIR) spectroscopy in developing predictive models of unreacted starch in corn. FT-NIR spectra of corn starch blends and ground corn from 4000 to 10000 cm-1 were calibrated against unreacted starch content, determined enzymatically, using various spectral preprocessing techniques such as multiplicative scatter correction (MSC), Savitzky-Golay (SG) derivative algorithms, and partial least squares regression (PLSR). Results showed the unreacted starch content in blends can be predicted with a low root mean square error of prediction (RMSEP) ranging from 1.29 to 1.95%, coefficient of regression (R2) of 0.97 to 0.98, and a ratio of performance to deviation (RPD) of 4.82 to 7.28. PLS regression models for unreacted starch content in dry and wet ground corn were equally promising with low RMSEPs of 1.13 to 2.23%, R2 values of 0.83 and 0.94, and RPD values of 1.55 to 2.16. These models are a valuable tool for high-throughput monitoring of unreacted starch during corn storage, handling, and processing.