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

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 mu m 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. Analysis: Novel design uses machine learning for characterising materials Scientists have developed low-cost particle size analyser (PSA) for conducting quality control and product testing in a range of industries, including the pharmaceutical, food and cosmetics. Currently, laser diffraction PSAs that use static light scattering to measure the size distribution of particles from hundreds of nanometres to several millimetres are the most widely used technology. However, these devices are generally large, expensive and complicated to operate, making them impractical for many industrial applications. Led by Valerio Pruneri from ICFO-The Institute of Photonic Sciences in Spain, a team of researchers has made a low-cost, miniaturised PSA capable of determining the volume median diameter of particles suspended in liquids. The novel design incorporates a collimated beam configuration using a commonly available image sensor to capture scattering images and machine learning to predict the particle size distribution.