Characterization data for printers is obtained by printing a test chart on the intended production substrate. In practice it is common for a different substrate to that used to obtain the characterization data to be used in proofing and in production, and this requires either reprinting and re-measuring the test chart or estimating new characterization data. Methods to do this exist for colorimetric characterization data, but with the increasing use of spectral data in the workflow, there is a need for a method that can be applied to spectral. reflectances. This paper proposes two different methods of adjusting printer spectral color characterization data for a change in substrates. In the first part, a Spectral Correction Technique was applied to spectral reflectance data obtained from different printers to predict a spectral color characterization data for an additional substrate. In the second part, the reference printing condition was used to adjust spectral color characterization data. The results were evaluated, and it was found that a good prediction is achieved with the use of machine learning.