Diffusion-weighted imaging provides information to study the brain microstructure. Several studies in the literature have shown that there is degeneracy in the estimated parameters for a commonly used microstructural model. B-tensor encoding is one of the strategies that has been proposed to solve the degeneracy. The combination of linear-spherical tensor encoding (LTE+STE) and linear-planar (LTE+PTE) have been utilized in previous works. In this paper, we compare different combinations of b-tensor encoding, (LTE+STE), linear-planar (LTE+PTE), planar-spherical (PTE+STE) and linear-planar-spherical (LTE+PTE+STE). We also compare the results of fit using a nonlinear least square algorithm and microstructure imaging of crossing (MIX) method. The results show that the combination of tensor encodings with MIX fitting algorithm leads to lower bias and higher precision in the parameter estimates than single tensor encoding.