RGB color correction and gamut limitations in smartphone-based kinetic analysis of chemical reactions

The variability in hardware specifications and environmental factors poses sig-nificant challenges to the use of smartphone cameras in analytical measurement. Towards time-resolved color analysis and reaction monitoring, we systematically quantified multiple sources of measurement uncertainty in smartphone-based color measurements, finding that while sensor repeatability is high (∆E < 0.5), lighting conditions and viewing angles can introduce substantial bias (∆E ver-sus reference colors increasing by up to 64% at oblique angles). We implemented and evaluated a matrix-based image color correction methodology using a color reference chart, reducing inter-device and lighting-dependent variations by 65-70% (quantified by the color change metric, ∆E). Moving beyond static image correction to video analysis, our approach was validated through the monitor-ing of Blue1 dye degradation kinetics using videos recorded on two different smartphones. Time-resolved and color-corrected measurements from both devices produced consistent kinetic profiles. Importantly, we identified a fundamental limitation in RGB-based colorimetry: highly saturated colors that exceed the sRGB color gamut create artificial discontinuities in kinetic profiles, manifesting as ”shouldering” effects not present in spectrophotometric data. Unlike previ-ous methods that focused on controlling environmental factors through custom enclosures, our time-resolved color correction methodology systematically quan-tifies and corrects for multiple sources of color bias across various smartphone models, enabling standardized measurements even in variable conditions. This advancement enhances the reliability of field-ready, smartphone-based colorimet-ric applications and establishes a framework for calibrating video-based reaction monitoring against established spectroscopic measurements.