Accurately previewing the appearance of a print job can make the difference between producing saleable output and wasting expensive materials and is a challenge to which a host of solutions already exist. However, what the majority of these have in common is that they base their predictions on the inputs to a printing system (e.g., continuous-tone data in ink channels) instead of its outputs (i.e., the halftone data that is then printed) and that they are only valid for a given set of choices already made in the printing system (e.g., color separation and halftoning). Alternatively, attempting to make appearance predictions using general-purpose models such as Kubelka Munk, Yule Nielsen and Neugebauer results in limited performance on systems whose behavior diverges from these models' assumptions, such as inkjet printing. As a result of such constraints, the resulting previews either work only under limited conditions or fail to predict some artifacts while erroneously predicting others that do not materialize in print. The approach presented here takes advantage of the flexibility of the HANS framework and the insights into spectral correlation to deliver a print preview solution that can be applied to any printing system, that allows for the variation of fundamental imaging choices without the need for re-computing model parameters and that delivers ICC-profile-level accuracy.