where νmix is a 16 × 1 column vector that contains the measured specific volumes for each mixture, w is a 16 × 6 matrix that contains the mass fraction of each component in each mixture, and νcomp is a 6 × 1 column vector that contains the specific volume of each component. We can regard νmix as a known from the measurements (see SI M), w as a known from the mixture preparation (see SI B), and νcomp as an unknown. Eq. ( ) does not have an exact solution since there are more mixtures than components and the νmix contain random measurement errors, but multiple linear least-squares regression can be used to find the νcomp that most closely solves it. We refer to the optimized component values in this solution as νopt. This analysis was performed, and the results are listed in Table (as densities). Fig. compares the measured ρmix (vertical axis) to the predicted ρmix using the linear blending rule, Eq. ( ), and the ρopt (horizontal axis). The agreement is very good, which shows that the functional form of the linear blending rule can fit the measured data.