If there are diffusing particles with different sizes (diffusion coefficients), it is common to fit to a function that is the sum of single component forms:
where the sum is over the number different sizes of particle, indexed by i, and  gives the weighting, which is related to the quantum yield and concentration of each type. This introduces new parameters, which makes the fitting more difficult as a higher-dimensional space must be searched. Nonlinear least square fitting typically becomes unstable with even a small number of s. A more robust fitting scheme, especially useful for polydisperse samples, is the Maximum Entropy Method.