# Nanoparticle Classification in Wide-field Interferometric Microscopy by Supervised Learning from Model

## TL;DR

This paper presents a supervised learning approach combined with interferometric microscopy for accurate nanoparticle classification based on defocus images, validated with experiments on gold and polystyrene nanospheres.

## Contribution

It introduces a novel combination of model-based supervised learning and wide-field interferometric microscopy for nanoparticle classification.

## Key findings

- High classification accuracy demonstrated experimentally
- Effective differentiation between gold and polystyrene nanospheres
- Potential for label-free nanoparticle detection and analysis

## Abstract

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.