Benchtop and handheld NIR and portable mid-infrared (MIR) spectrometers were evaluated as rapid methods for differentiating between organic and conventional cornmeal and to measure quality parameters of cornmeal used for production of snack foods. Twenty-seven conventional and eleven organic cornmeal samples were obtained from a local manufacturer of grain-based products. Reference quality parameters measured included moisture content, ash content, pasting properties and particle size. Soft independent modeling of class analogy (SIMCA) analysis accurately classified between organic and conventional cornmeal samples (interclass distance > 3.7) based on differences in the C=O signal associated with side chain vibrations of acidic amino acids. Residual predictive deviation (RPD) values for partial least squares regression (PLSR) models developed, ranged between 2.3 and 9.6. Overall, our data supports the capability of infrared systems to classify between organic and conventional cornmeal, and to predict important quality attributes of cornmeal for the snack food industry.