Point-of-care colorimetric detection with a smartphone

TLDR

Paper‑based immunoassays are emerging as powerful, low‑cost diagnostics for resource‑limited settings, yet inexpensive quantification methods using desktop scanners lack portability and camera‑based approaches suffer from variable ambient lighting. The study introduces a smartphone‑based colorimetric assay quantification that delivers high‑accuracy measurements across diverse lighting conditions and can be integrated into a one‑click app for use without specialized training. The method employs chromaticity values instead of raw RGB intensities to construct calibration curves of analyte concentrations, and incorporates a calibration technique that corrects for ambient light variability from common sources such as sunlight, fluorescent, or LED lighting. Results show high‑accuracy pH measurements over a linear range of 1–12, comparable to desktop scanners or silicon photodetectors, and the technique remains effective under various common light sources.

Abstract

Paper-based immunoassays are becoming powerful and low-cost diagnostic tools, especially in resource-limited settings. Inexpensive methods for quantifying these assays have been shown using desktop scanners, which lack portability, and cameras, which suffer from the ever changing ambient light conditions. In this work, we introduce a novel approach of quantifying colors of colorimetric diagnostic assays with a smartphone that allows high accuracy measurements in a wide range of ambient conditions, making it a truly portable system. Instead of directly using the red, green, and blue (RGB) intensities of the color images taken by a smartphone camera, we use chromaticity values to construct calibration curves of analyte concentrations. We demonstrate the high accuracy of this approach in pH measurements with linear response ranges of 1-12. These results are comparable to those reported using a desktop scanner or silicon photodetectors. To make the approach adoptable under different lighting conditions, we developed a calibration technique to compensate for measurement errors due to variability in ambient light. This technique is applicable to a number of common light sources, such as sun light, fluorescent light, or smartphone LED light. Ultimately, the entire approach can be integrated in an "app" to enable one-click reading, making our smartphone based approach operable without any professional training or complex instrumentation.