In this article we compared the Kronecker model against the Intercept model for a mixture experiment. We investigated conditioning using the variance inflation factor, the maximum and minimum eigenvalues of the information matrix, and the conditional number, to assess conditioning. The pseudocomponents transformation are also discussed. Practical examples are provided to support the conclusions. Recommendations regarding when to use the Kronecker and Intercept modeling approaches are provided. Conditioning was assessed in both adjusted models using the variance inflation factor and condition number. Evidence was given that, for the Intercept model, the choice of which component is replaced for the constant term is crucial in the sense on numerical stability.