A perennial and very difficult problem to solve in deep learning is the question of how to interpret what the model is learning. We address this issue in the following manner: by recognizing that PCA performed on the latent space both creates a lower-dimensional representation which is more easily visualized and also creates a ranking of linear superpositions of the latent dimensions, we argue that a latent space that is less distorted by a PCA analysis is a more interpretable one, and we assess the models on this basis. This also addresses the question of how meaningful the observation of ordering in a linear projection of the latent space is, which, as far as we are aware, has not previously been addressed in the literature. Finally, we consider a set of physicochemical variables of interest and identify potential "bridge variables" associated with different PCs, to aid in their interpretation.