The particle size distribution can also be obtained using the autocorrelation function. However, polydisperse samples are not well resolved by the cumulant fit analysis. Thus, the combination of non-negative least squares (NNLS) algorithms with regularization methods, such as the Tikhonov regularization, can be used to resolve multimodal samples. An important feature of the NNLS optimization is the regularization term used to identify specific solutions and minimize the deviation between the measure data and the fit. There is no ideal regularization term that is suitable for all samples. The shape of this term can determine if the solution will represent a general broad distribution with small number of peaks or if narrow and discrete populations will be fit. Alternatively, the calculation of the particle size distribution is performed using the CONTIN algorithm.