Smartphone-based colorimetric detection system for portable health tracking.

Area of Science:

• Biomedical Engineering
• Analytical Chemistry
• Mobile Health

Background:

• Colorimetric urinalysis test strips offer cost-effective, practical at-home health monitoring.
• Integrating these sensors with digital systems, especially smartphones, faces challenges due to ambient light interference and low sensitivity.
• Existing smartphone-based optical readout systems are often error-prone, limiting their daily use.

Purpose of the Study:

• To develop a novel smartphone application (Colourine) for accurate digital readout of colorimetric signals from urinalysis test strips.
• To overcome limitations of ambient light noise and improve sensitivity in smartphone-based colorimetric analysis.
• To validate the application's performance for detecting pH, proteins, and glucose in urine samples.

Main Methods:

• Developed an Android OS smartphone application (Colourine) for colorimetric signal readout.
• Implemented a pre-calibration step using a reference chart and CIE-RGB-to-HSV color space transformation to minimize environmental lighting noise.
• Tested the application with commercial urinalysis test strips for pH, proteins, and glucose detection under varying ambient light conditions (100-400 lx).

Main Results:

• The Colourine application demonstrated reliable output across a tested ambient light range.
• Achieved limits of detection (LOD) of 0.13 pH units, 7.5 mg dL^-1 for proteins, and 22 mg dL^-1 for glucose.
• The average LOD was improved approximately 2.8-fold compared to traditional visual assessment methods.

Conclusions:

• The Colourine smartphone application offers a robust and accurate method for digital readout of colorimetric urinalysis test strips.
• The integrated pre-calibration and color space transformation effectively mitigate ambient light interference, enhancing measurement reliability.
• This technology holds potential for improving accessible and precise at-home health monitoring.