Gamma Correction and Color Space Transformations for Quantitative Analysis of Electrochemiluminescence Images Using Smartphone Cameras

Quantitative imaging of luminescent signals, ranging from electrochemiluminescence (ECL) and chemiluminescence to colorimetric assays, is increasingly performed using consumer-grade digital cameras and smartphones. However, device-dependent variability, nonlinear signal encoding, and the absence of standardized workflows hinder reproducibility and quantification accuracy. This work presents a generalized methodology for robust signal quantification in luminescent systems using digital imaging, with ECL as a model case. By combining synchronized electrochemical control, manual optimization of imaging parameters, gamma correction, and color space transformations, accurate device-independent analysis is enabled. Using Ru-(bpy)₃ ²⁺/TPrA as a test system, we evaluate RGB, CIEXYZ, and CIELAB color spaces, identifying optimal channels for sensitivity and dynamic range. Our performance assessment underscores the importance of transfer function selection and supports both linear and nonlinear quantification models. Results show that linearized r and X color channels offer broad dynamic ranges with moderate sensitivity, while encoded R and a* channels provide higher sensitivity at low concentrations, requiring nonlinear modeling to extend their quantification range. This scalable approach enables standardized, high-throughput optical analysis using low-cost camera platforms, with broad applications in diagnostics, biosensing, and analytical chemistry.