Technical Abstract: RVAs (Rapid Visco Analyzers) are widely used in assessing cooking and processing characteristics in rice. However, traditional parameters (peak viscosity, final viscosity, breakdown, consistency, and setback) rely on several factors. This makes them difficult to predict spectroscopically. For this reason we have investigated methods by which to obtain new RVA parameters that are more predictable. Short, medium, and long grain rice flour samples were used for NIR and RVA measurements. The content of amylose ranged from 0.41% to 24.90% (w/w) and that of protein ranged from 4.89% to 11.35%. Moisture in rice flour samples was approximately 12%. PLS1 (Partial Least Squares Regression) models were created for NIR prediction of RVA parameters. NIR absorptions and RVA viscosity intensities were used in a PLS2 regression model. The PLS2 model resulted in the construction of a 2D matrix map of regression coefficients. The new wparameters were found to correlate more strongly with the NIR (1,100-2,500 nm) region than traditional parameters. This finding suggests that NIR can be used to predict rheological properties.