An ultra-compact particle size analyser using a CMOS image sensor and machine learning.,Light: Science & Applications

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An ultra-compact particle size analyser using a CMOS image sensor and machine learning.
Light: Science & Applications ( IF 20.6 ) Pub Date : 2020-02-12 , DOI: 10.1038/s41377-020-0255-6
Rubaiya Hussain ₁ , Mehmet Alican Noyan _{1,

2} , Getinet Woyessa ₃ , Rodrigo R Retamal Marín ₄ , Pedro Antonio Martinez ₁ , Faiz M Mahdi ₅ , Vittoria Finazzi ₁ , Thomas A Hazlehurst ₅ , Timothy N Hunter ₅ , Tomeu Coll ₁ , Michael Stintz ₄ , Frans Muller ₅ , Georgios Chalkias ₆ , Valerio Pruneri _{1,

7}

Affiliation

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.

中文翻译：

使用CMOS图像传感器和机器学习的超紧凑型粒度分析仪。

光散射是一项基本属性，可以用来创建基本的设备，例如粒子分析仪。最常见的粒度分析仪依赖于测量来自被激光束照射的样品的角度相关的衍射光。与其他非基于光的同类产品相比，这种激光衍射方案可提供精度，但这样做会牺牲尺寸，复杂性和成本。在本文中，我们将介绍使用消费电子相机和机器学习在准直光束配置中的新型粒度分析仪的概念。关键的新颖之处在于它是一种小形状因数的角度空间滤波器，它可以收集由粒子散射的光，直至达到预定的离散角度。该滤光片与发光二极管和互补金属氧化物半导体图像传感器阵列组合在一起，以获取角度分解的散射图像。根据这些图像，机器学习模型可以预测粒子的体积中值直径。为了验证所提出的装置，在悬浮液中以几种浓度测量了直径范围为13至125 µm的玻璃珠。对于没有和以浓度为输入参数的情况，我们能够校正多重散射效应并预测平均粒径绝对误差为5.09％和2.5％的粒径。仅分析球形颗粒时，前者的误差显着降低（0.72％）。鉴于它是紧凑的（约10厘米），并使用低成本的消费类电子产品制造，

更新日期：2020-02-12

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