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An ultra-compact particle size analyser using a CMOS image sensor and machine learning.

Rubaiya Hussain¹, Mehmet Alican Noyan^1,2, Getinet Woyessa³

¹1ICFO- Institut de Ciències Fotòniques, The Barcelona Institute of Science and Technology, 08860 Castelldefels (Barcelona), Spain.

Light, Science & Applications

|March 5, 2020

Summary

A new compact particle size analyzer uses a camera and machine learning to measure particle size, offering a low-cost alternative to traditional laser diffraction methods. This device demonstrates high accuracy, even with multiple scattering effects.

Keywords:

Imaging and sensing Optics and photonics

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Area of Science:

Optics and Photonics
Machine Learning Applications
Materials Science

Background:

Traditional laser diffraction particle size analyzers are precise but large, complex, and costly.
There is a need for compact, affordable particle analysis tools for diverse applications.

Purpose of the Study:

To introduce a novel, compact particle size analyzer utilizing consumer electronics and machine learning.
To validate the performance of this new device against established methods.

Main Methods:

Development of a collimated beam configuration with an angular spatial filter.
Integration of a light-emitting diode (LED) and a complementary metal-oxide-semiconductor (CMOS) image sensor.
Application of a machine learning model to predict volume median diameter from scattering images.
Validation using glass beads (13-125 µm) at various concentrations.

Main Results:

Accurate prediction of particle size with low mean absolute percentage errors (5.09% without concentration, 2.5% with concentration).
Significant reduction in error (0.72%) when analyzing only spherical particles.
Demonstrated ability to correct for multiple scattering effects.

Conclusions:

The proposed device is compact (approx. 10 cm) and built with low-cost components.
It offers a viable, cost-effective alternative to traditional particle size analyzers.
Potential applications include online and in-line industrial process monitoring outside standard laboratory settings.