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

Rubaiya Hussain; Mehmet Alican Noyan; Getinet Woyessa; Rodrigo R. Retamal Marín; Pedro Antonio Martinez; Faiz M. Mahdi; Vittoria Finazzi; Thomas A. Hazlehurst; Timothy N. Hunter; Tomeu Coll; Michael Stintz; Frans Muller; Georgios Chalkias; Valerio Pruneri

doi:10.1038/s41377-020-0255-6

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

Article

- An ultra-compact particle size analyser using a CMOS image sensor and machine learning
- Light: Science & Applications Vol. 9, Issue 1, Pages: 176-186(2020)
- Affiliations：
  
  1.ICFO-Institut de Ciències Fotòniques, The Barcelona Institute of Science and Technology, 08860 Castelldefels (Barcelona), Spain
  2.Ipsumio B.V., High Tech Campus, 5656 AE, Eindhoven, Netherlands
  3.Department of Photonics Engineering, Technical University of Denmark, DK-2800 Kgs Lyngby, Denmark
  4.Research Group Mechanical Process Engineering, Institute of Process Engineering and Environmental Technology, Technische Universität Dresden, Münchner Platz 3, D-01062 Dresden, Germany
  5.School of Chemical and Process Engineering, University of Leeds, Leeds LS2 9JT, UK
  6.IRIS Technology Solutions, SL, 08860 Castelldefels (Barcelona), Spain
  7.ICREA-Institució Catalana de Recerca i Estudis Avan?ats, 08010 Barcelona, Spain
- Author bio：
  
  Valerio Pruneri (valerio.pruneri@icfo.eu)
- Funds：
- DOI：10.1038/s41377-020-0255-6
  CLC：
- Published：2020，
  
  Published Online：12 February 2020，
  
  Received：06 June 2019，
  
  Revised：09 January 2020，
  
  Accepted：27 January 2020
- Accepted：
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Hussain, R. et al. An ultra-compact particle size analyser using a CMOS image sensor and machine learning. Light: Science & Applications, 9, 176–186 (2020).
DOI：

Hussain, R. et al. An ultra-compact particle size analyser using a CMOS image sensor and machine learning. Light: Science & Applications, 9, 176–186 (2020). DOI： 10.1038/s41377-020-0255-6.

摘要

Abstract

Light scattering is a fundamental property that can be exploited to create essential devices such as particle analysers. The most common particle size analyser relies on measuring the angle-dependent diffracted light from a sample illuminated by a laser beam. Compared to other non-light-based counterparts

such a laser diffraction scheme offers precision

but it does so at the expense of size

complexity and cost. In this paper

we introduce the concept of a new particle size analyser in a collimated beam configuration using a consumer electronic camera and machine learning. The key novelty is a small form factor angular spatial filter that allows for the collection of light scattered by the particles up to predefined discrete angles. The filter is combined with a light-emitting diode and a complementary metal-oxide-semiconductor image sensor array to acquire angularly resolved scattering images. From these images

a machine learning model predicts the volume median diameter of the particles. To validate the proposed device

glass beads with diameters ranging from 13 to 125 µm were measured in suspension at several concentrations. We were able to correct for multiple scattering effects and predict the particle size with mean absolute percentage errors of 5.09% and 2.5% for the cases without and with concentration as an input parameter

respectively. When only spherical particles were analysed

the former error was significantly reduced (0.72%). Given that it is compact (on the order of ten cm) and built with low-cost consumer electronics

the newly designed particle size analyser has significant potential for use outside a standard laboratory

for example

in online and in-line industrial process monitoring.

关键词

Keywords

references

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