To take advantage of the consistent and automated measurement capability of Computer Vision Systems (CVS), an initial CVS with inclined imaging of the food object was developed to measure its color in a real-time food process. The RGB-L*a*b* color-space transformation model of the CVS was obtained by multiple linear regression. The impact of training data and regularization for high-order regression models was thoroughly evaluated using cross-validation results and mean color error. A case study with roasted rice highlighted the importance of the training data size, the choice of samples in the appropriate color gamut, and the inclusion of representative color samples for a specific target object. The optimal cubic Ridge regression model had the CIEDE2000 mean color errors of 1.73, 1.11, and 1.33 when respectively evaluated with the training dataset and two testing rice datasets. The higher testing accuracy reconfirmed the appropriate training data preparation and demonstrated the benefits of regularization in regression. Based on various performance indices, the adopted model outperformed those reported in the literature. Therefore, the proposed CVS could be reproduced for color characterization of food products in automated and real-time processes.