Design and Fabrication of a Low-Cost Colorimetric Reader for Adaptable Diagnostics 

Presenter: Larissa Vinokurova

Faculty Sponsor: Kimberly Hamad-Schifferli

School: UMass Boston

Research Area: Disease Detection, Prevention & Treatment

Session: Poster Session 3, 1:15 PM - 2:00 PM, Auditorium, A10

ABSTRACT

Lighting variation, angle of incidence, viewing angle, and camera distance may affect test result interpretation in an innovative colorimetric paper-based rapid diagnostic method, where 3D-printed reconfigurable blocks hold test strips designed to detect different infectious diseases depending on the blocks’ setup. This study aims to optimize the identification of infectious disease test results by standardizing image capture conditions, reducing glare, and enhancing color accuracy for improved diagnostic reliability. Sample images were captured under varying conditions, including different distances, angles of incidence, and viewing angles. These images were analyzed in MATLAB for color clarity, glare reduction, and optimal contrast on test strips. A prototype imaging system was designed incorporating custom lighting and a camera enclosure, significantly reducing glare and reflections. A 3D-printed imaging enclosure was developed to facilitate standardized image capture using a mobile phone camera. This low-cost, adaptable system improved the reliability of colorimetric test readings by minimizing reflections, and variations in lighting and distance. The presence and intensity of test strip colors were analyzed quantitatively, enhancing the accuracy of result interpretation. The innovative use of reconfigurable blocks and controlled imaging conditions demonstrated a measurable improvement in test reliability and reproducibility, ensuring that color variations more accurately correspond to diagnostic outcomes. The findings in this research highlight the importance of standardized image acquisition techniques in reducing errors associated with environmental lighting and viewing conditions. This cost-effective solution paves the way for improved diagnostic accessibility and reliability, particularly in resource-limited settings.

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