Identification of Model Particle Mixtures Using Machine-Learning-Assisted Laser Diffraction
@article{Villegas2021IdentificationOM, title={Identification of Model Particle Mixtures Using Machine-Learning-Assisted Laser Diffraction}, author={Arturo Villegas and Mario Alan Quiroz-Ju{\'a}rez and Alfred B. U’Ren and Juan P. Torres and Roberto de J. Le{\'o}n-Montiel}, journal={Photonics}, year={2021} }
We put forward and demonstrate with model particles a smart laser-diffraction analysis technique aimed at particle mixture identification. We retrieve information about the size, shape, and ratio concentration of two-component heterogeneous model particle mixtures with an accuracy above 92%. We verify the method by detecting arrays of randomly located model particles with different shapes generated with a Digital Micromirror Device (DMD). In contrast to commonly-used laser diffraction schemes…
References
SHOWING 1-10 OF 76 REFERENCES
An ultra-compact particle size analyser using a CMOS image sensor and machine learning
- Environmental ScienceLight, science & applications
- 2020
A team of researchers has made a low-cost, miniaturised PSA capable of determining the volume median diameter of particles suspended in liquids and incorporates a collimated beam configuration using a commonly available image sensor to capture scattering images and machine learning to predict the particle size distribution.
Use of neural networks in the analysis of particle size distribution by laser diffraction: tests with different particle systems
- Materials Science
- 2002
Machine-learning techniques for fast and accurate feature localization in holograms of colloidal particles.
- Computer ScienceOptics express
- 2018
It is demonstrated that two popular machine-learning algorithms, cascade classifiers and deep convolutional neural networks (CNN) can solve the feature-localization problem orders of magnitude faster than current state-of-the-art techniques.
Use of neural networks in the analysis of particle size distributions by laser diffraction
- Materials Science
- 1997
Machine-learning-based computationally efficient particle size distribution retrieval from bulk optical properties.
- Computer ScienceApplied optics
- 2020
An efficient numerical approach to solving the inverse scattering problem by accelerating the calculation of bulk optical properties based on machine learning and the particle swarm optimization algorithm is employed to optimize the particle size distribution parameters.
CATCH: Characterizing and Tracking Colloids Holographically Using Deep Neural Networks.
- PhysicsThe journal of physical chemistry. B
- 2020
Characterizing and Tracking Colloids Holographically (CATCH) with deep convolutional neural networks is fast enough for real-time applications and otherwise outperforms conventional analytical algorithms, particularly for heterogeneous and crowded samples.
Extending laser diffraction for particle shape characterization: technical aspects and application
- Physics
- 2001
Convolutional neural network applied for nanoparticle classification using coherent scatterometry data.
- Computer ScienceApplied optics
- 2020
This work trained a convolutional neural network, including its architecture optimization, and achieved 95% accurate results, compared to an existing method based on line-by-line search and thresholding, demonstrating up to a twofold enhanced performance in particle classification.