Real-time spectroscopic methods can provide a valuable window into food manufacturing to permit optimization of production rate, quality and safety. There is a need for cutting edge sensor technology directed at improving efficiency, throughput and reliability of critical processes. The aim of the research was to evaluate the feasibility of infrared systems combined with chemometric analysis to develop rapid methods for determination of sugars in cereal products. Samples were ground and spectra were collected using a mid-infrared (MIR) spectrometer equipped with a triple-bounce ZnSe MIRacle attenuated total reflectance accessory or Fourier transform near infrared (NIR) system equipped with a diffuse reflection-integrating sphere. Sugar contents were determined using a reference HPLC method. Partial least squares regression (PLSR) was used to create cross-validated calibration models. The predictability of the models was evaluated on an independent set of samples and compared with reference techniques. MIR and NIR spectra showed characteristic absorption bands for sugars, and generated excellent PLSR models (sucrose: SEP < 1.7% and r > 0.96). Multivariate models accurately and precisely predicted sugar level in snacks allowing for rapid analysis. This simple technique allows for reliable prediction of quality parameters, and automation enabling food manufacturers for early corrective actions that will ultimately save time and money while establishing a uniform quality. © 2012 Institute of Food Technologists®.