Particle Identification Using Light Scattering

We discuss some experience of solving an inverse light scattering problem for single, spherical, homogeneous particles using least squares global optimization. If there is significant noise in the data, the particle corresponding to the “best” solution may not correspond well to the “actual” particle. One way of overcoming this difficulty involves the use of peak positions in the experimental data as a means of distinguishing genuine from spurious solutions. We introduce two composite approaches which combine conventional data fitting with peak-matching and show that they lead to a more robust identification procedure.