Prediction of Arsenic Concentration in Water Samples Using Digital Imaging Colorimetry and Multi-Variable Regression

Arsenic is perhaps the most harmful components and may be found in drinking water. In this work, a convenient and low-cost gadget is designed and connected to smartphone to predict arsenic concentration. The smartphone captures the images of color change and analyzes data in RGB color space. The color change is the result of functionalized gold nanoparticle aggregations in the presence of arsenic ions. By implementing a multi-variable regression model, the captured data is converted into the arsenic concentration values. Under optimum analytical conditions, the proposed algorithm provides better detection limit and linearity as compared to other studies. The nanoprobe has high sensitivity to arsenic, with detection limit of 0.45 ppb at the linear range of 1–7500 ppb and a mean squared error of 0.24. The quantitative results agreed with those obtained by the reference ICP-MS method at a 94 % confidence level and can be used for on-site detection of low-content arsenic ions.