Abstract

The inverse scattering problem of non-spherical particle size estimation is solved using a series of supervised machine learning models trained on a library of light scattering data. By establishing a large library with spheres and spheroids as fundamental shapes and through optimization of model hyperparameters, the trained models are able to accurately estimate a precise equivalent volume sphere radius of particles from an external database and simulations, with root mean square errors of 2.6% and 1.9% for the external and simulated particles, respectively. It was found that classification via a $ k $-nearest neighbor model and refinement via a trained ensemble regression model performed best for equivalent volume measurements.

© 2020 Optical Society of America

Full Article  |  PDF Article
More Like This
Machine-learning-based computationally efficient particle size distribution retrieval from bulk optical properties

Ruhui Jia, Xiaohao Zhang, Fenping Cui, Gongye Chen, Haomiao Li, Haochen Peng, Zhaolou Cao, and Shixin Pei
Appl. Opt. 59(24) 7284-7291 (2020)

Machine Learning Models for Estimating Quality of Transmission in DWDM Networks

Rui Manuel Morais and João Pedro
J. Opt. Commun. Netw. 10(10) D84-D99 (2018)

Use of light scattering to estimate the fraction of spherical particles in a mixture

Paul Rochon, T. J. Racey, and M. Zeller
Appl. Opt. 27(15) 3295-3298 (1988)

References

You do not have subscription access to this journal. Citation lists with outbound citation links are available to subscribers only. You may subscribe either as an OSA member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access OSA Member Subscription

Cited By

You do not have subscription access to this journal. Cited by links are available to subscribers only. You may subscribe either as an OSA member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access OSA Member Subscription

Figures (9)

You do not have subscription access to this journal. Figure files are available to subscribers only. You may subscribe either as an OSA member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access OSA Member Subscription

Tables (6)

You do not have subscription access to this journal. Article tables are available to subscribers only. You may subscribe either as an OSA member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access OSA Member Subscription

Equations (4)

You do not have subscription access to this journal. Equations are available to subscribers only. You may subscribe either as an OSA member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access OSA Member Subscription

Metrics

You do not have subscription access to this journal. Article level metrics are available to subscribers only. You may subscribe either as an OSA member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access OSA Member Subscription