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

• 作者: 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
• 来源期刊: 光：科学与应用（英文版）
• 年，卷（期）: 2020,（1）
• 所属期刊栏目: Original Articles
• 页码范围: 176-186
• 页数: 11页
• 语种: 英文

摘要

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.