A printer characterization attempts to map, in both directions, corresponding points in colorant and colorimetric spaces. Two limiting approaches are used: analytical models based on a small number of samples, and direct measurement and interpolation requiring many samples. For six-color printers, the former approach often has insufficient accuracy whereas the latter approach requires an excessive number of samples. An intermediate approach was used to characterize a CMYKGO ink jet printer, the Cellular-Yule-Nielsen-Spectral-Neugebauer (CYNSN) model. This model included an optimized Yule-Nielsen n value and onedimensional look-up tables between digital data and effective area coverage for each colorant. Each colorant was divided into three subspaces, or cells, requiring the selection of two intermediate values and fixed endpoints of 0% and 100% effective area coverage. An optimization was performed that determined these intermediate values by minimizing the maximum spectral error when one-colorant CYNSN models were used to predict 256-step ramps. This technique enabled a considerable reduction of the total number of required samples from several hundreds of thousands to 4,096, the required number of cellular Neugebauer primaries. Of these colors, only 1,024 could be printed; the remainder was non-printable due to inkblots. A third optimization synthesized the spectral properties of the non-printable cellular primaries using weighted spectral regression, the weighting a function of colorant-space location. The CYNSN model based on these three optimizations was able to predict 600 random colors sampling the colorimetric gamut to an average spectral RMS error of less than 0.5% and ΔE₀₀ of less than 1.0. The color gamut achievable using the synthesized spectra was 54% larger in colorant space and 15% larger in CIELAB space than that achievable when limiting the CYNSN model to printable cellular primaries.