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

The variability in hardware specifications and environmental factors poses significant 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 ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>Δ</mml:mi> <mml:mi>E</mml:mi> <mml:mo><</mml:mo> <mml:mn>0.5</mml:mn></mml:mrow> </mml:math> ), lighting conditions and viewing angles can introduce substantial bias ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>Δ</mml:mi> <mml:mi>E</mml:mi></mml:mrow> </mml:math> versus 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, <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>Δ</mml:mi> <mml:mi>E</mml:mi></mml:mrow> </mml:math> ). Moving beyond static image correction to video analysis, our approach was validated through the monitoring 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 previous methods that focused on controlling environmental factors through custom enclosures, our time-resolved color correction methodology systematically quantifies 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 colorimetric applications and establishes a framework for calibrating video-based reaction monitoring against established spectroscopic measurements.