Characterization of optically soft spheroidal particles by multiangle light-scattering data by use of the neural-networks method

Abstract

A method for evaluating the size of optically soft spheroidal particles by use of the angular structure of scattered light is proposed. It is based on the use of multilevel neural networks with a linear activation function. The retrieval errors of radius R of the equivolume sphere and aspect ratio e are investigated. The ranges of the size of R, e, and the refractive index are 0.3-1.51 microns, 0.2-1, and 1.01-1.02, respectively. The retrieval errors of the equivolume radius and aspect ratio are 0.004 micron and 0.02, respectively, for a three-level neural network (at a precisely measured angular distribution of scattered light). The retrieval errors of R and e for a one-level neural network are 2-5 times greater. The errors for a multilevel neural network increase faster than those for a single-level network.

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