Particle Size Distribution from Combined Light Scattering Measurements. A Neural Network Approach for Solving the Inverse Problem

A method is proposed for estimating the particle size distribution (PSD) of a latex with particle diameters in the sub-micrometer range, from combined elastic light scattering (ELS) and dynamic light scattering (DLS) measurements. The method is implemented through a general regression neural network (GRNN), that estimates the PSD from the ELS measurement carried out at several angles together with the average diameters of the PSD predicted by the DLS measurement at the same angles. The GRNN was trained with several measurements simulated on the basis of typical asymmetric PSDs. The ability of the trained GRNN was tested on the basis of two synthetic examples. The estimated PSDs are more accurate than those obtained through standard numerical techniques for `ill-conditioned' inverse problems.