Spectral Optimization of Programmable LED Illumination for Smartphone-Based Colorimetric Biosensor Readout

Smartphone-based colorimetric biosensor readout is strongly influenced by illumination, yet lighting conditions are often characterized mainly by illuminance rather than by their spectral composition. This study investigates the spectral, photometric, colorimetric, and energetic effects of programmable LED illumination on smartphone colorimetric readout under controlled acquisition conditions. Twenty lighting scenarios were generated using three independently controlled color-capable LED luminaires and characterized by spectral irradiance, illuminance, chromaticity, color rendering, and electrical power. A Samsung Galaxy S20 FE was used to acquire JPEG and RAW images of a standardized 24-patch ColorChecker target used as a controlled colorimetric reference for evaluating the optical readout methodology, and colorimetric deviation was quantified using CIEDE2000. Mean ΔE₀₀ ranged from 11.73 to 42.33 across the investigated scenarios. Two conditions differing by less than 1% in illuminance produced a 37% difference in mean colorimetric error, demonstrating that illuminance alone does not determine readout accuracy. An illuminance-only regression model yielded R² = 0.819, whereas inclusion of a relative spectral descriptor increased R² to 0.919. High-output conditions also revealed substantial JPEG clipping despite negligible RAW saturation. These results show that reliable smartphone colorimetric readout requires joint consideration of illumination intensity, spectral composition, electrical demand and image-saturation constraints.