Improving sensitivity of mercury detection using learning based smartphone colorimetry

Abstract

Detection of various contaminations in drinking water such as heavy metal ions and toxic chemicals is costly, time-consuming and requires an accompanying computing device to capture and analyze the data. Hence, there is an extensive need for a rapid, user-friendly, cost-effective, sensitive and ubiquitous detection technique. Smartphones are an effective means to measure, analyze and share the results. In this work, a gadget was designed and printed using a lightweight 3D material, which can be attached to any smartphone and integrated with optical components. A full color TFT LCD display was used as the uniform source of any color of light. Aptamer conjugated gold nanoparticles were employed to determine the concentration of Hg2+ as the basis of a colorimetric sensor. Interaction between the aptamer and the analytes leads to a color change in the solution due to aggregation of gold nanoparticles. For the corresponding color change detection, a novel image processing protocol using RGB value was introduced for each captured image. Multiple linear regression analysis was also exploited to achieve a better sensor response model. Light source enhancement, colorimetry at more points of visible spectrum (470, 540, 640 nm) and a powerful post process technique including machine learning made it possible to obtain an excellent level of sensitivity (1 nM–0.2 ppb).