It is common practice for digital image capture systems to use a small number of de-facto-standard test targets. Unfortunately, however, color (spectral-) characteristics of the colorants used may differ from those for the population of object/scenes to be captured. This can lead to poor color calibration of the system. A second limitation of current color-capture evaluation arises when the same set of color stimuli (color patches) are used to calibrate the color capture and to evaluate the residual color errors. When the same color-target is used, the reported color-encoding errors will usually be lower than those observed in normal image capture. This is because we are, in effect, 'teaching to the test', as when a student is prepared for test taking, rather than subject mastery. We can approach this under-reporting of color error as a measurement bias. We can treat color-correction (e.g. by a color-profile) as being a statistical model relating the detected image values and their intended ('correct') pixel values. Using a statistical approach we adopt a validation method aimed at determining the extent to which this model relationship between variables (the regression model) provides an acceptable description of the data. For our color-imaging case, the equivalent step would be to test the computed color-correction (ICC profile) with color patches that are independent of those used to build the profile. We demonstrate a candidate strategy for selecting these test colors, and an example of a validation set of colors chosen to be distinct from the calibration set in the popular ColorChecker SG.