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.
Highlights
Particle size analysis based on light scattering has widespread application in many fields, as it allows relatively easy optical characterisation of samples enabling improved quality control of products in many industries including pharmaceutical, food, cosmetic, polymer production, etc.[1,2,3]
We propose a novel low-cost and miniaturised particle size analysers (PSAs) in a collimated beam configuration using a complementary metal-oxide-semiconductor (CMOS) image sensor and an machine learning (ML) model based on a random forest algorithm[23]
By analysing the raw angular spatial filter (ASF) images obtained from the CMOS image sensor array, we show how multiple scattering becomes prominent at high concentrations depending on the particle size being measured and how the random forest algorithm can correct this issue
Summary
Particle size analysis based on light scattering has widespread application in many fields, as it allows relatively easy optical characterisation of samples enabling improved quality control of products in many industries including pharmaceutical, food, cosmetic, polymer production, etc.[1,2,3]. Dynamic light scattering (DLS)[4] has become an industry standard technique. This method analyses the fluctuations of scattered light by particles in suspension when illuminated with a laser to determine the velocity of the Brownian motion, which can be used to obtain the hydrodynamic size of particles using the StokesEinstein relationship. NTA measures the hydrodynamic size of particles from the diffusion coefficient but Hussain et al Light: Science & Applications (2020)9:21 is capable of overcoming some of the limitations posed by DLS5,6
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