Improving sensitivity of mercury detection using learning based smartphone colorimetry

• Published in: Sensors and actuators. B, Chemical Chemical, 2019-11, Vol.298, p.126942, Article 126942
• Main Authors: Sajed, S., Arefi, F., Kolahdouz, M., Sadeghi, M.A.
• Format: Article
• Language: English
• Subjects: Change detection; Colorimetry; Computing costs; Drinking water; Gold; Gold nanoparticles; Heavy metals; Image detection; Image processing; Lab-on-a-phone; Light sources; Localized surface plasmon resonance; Machine learning; Mercury (metal); Mercury concentration; Multiple linear regression; Nanoparticles; Optical components; Organic chemistry; Regression analysis; RGB value; Sensitivity; Smartphones; Visible spectrum
• ISSN: 0925-4005; 1873-3077
• DOI: 10.1016/j.snb.2019.126942

[Display omitted] •A learning-based model explains the Hg concentration as a function of R, G and B.•A uniform irradiance condition achieved by using a TFT LCD for colorimetry.•LSPR peak variation is detectable using the camera of a smart phone.•Excellent sensitivity (0.2 ppb) and selective detection are advantages of our gadget.•Reliable water qualification method introduced for people’s daily life. 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).