  Interference enhanced wide-field nanoparticle imaging is a highly sensitive
technique that has found numerous applications in labeled and label-free
sub-diffraction-limited pathogen detection. It also provides unique
opportunities for nanoparticle classification upon detection. More specif-
ically, the nanoparticle defocus images result in a particle-specific response
that can be of great utility for nanoparticle classification, particularly
based on type and size. In this work, we com- bine a model based supervised
learning algorithm with a wide-field common-path interferometric microscopy
method to achieve accurate nanoparticle classification. We verify our
classification schemes experimentally by using gold and polystyrene
nanospheres.
