When chemistry is too colourful: gamut clipping in 8-bit sRGB risks misinterpretation of camera-based chemical analysis.

Authors

Calum Fyfe, Shengkai Yu, Marc Reid

Published in

Digital discovery. Jun 29, 2026. Epub Jun 29, 2026.

Abstract

Digital cameras are increasingly utilised to capture visual changes in chemical processes. Monitoring colour with computer vision tools serves as a valuable proxy for monitoring bulk chemical changes. Most consumer-grade cameras digitise the colours captured using the sRGB colour space. Despite the ubiquity of video cameras, they (by design) restrict the range of colour information that can be stored. With regards to chemical analysis and process monitoring, the limitations of using the sRGB gamut have not been addressed. When real-world vivid colours one attempts to record lie outside the bounds of sRGB, the digitisation of those colours can result in distortion, clipping, or loss of chemically relevant colour data. Ultimately, these sRGB gamut limitations risk the chemist misinterpreting the data they collect from a camera. In this paper, we examined the visible spectrum of a series of common dyes and determined their colours spectroscopically, without the limitation of sRGB encoding. We investigated how sRGB encoding affects the interpretation of time-series data from theoretical colour changes in five dyes and validated these findings using colour values extracted directly from camera images of the same dye solutions. Highly saturated chemical samples exceeded the sRGB colour gamut, causing colour distortions and structural breaks in reaction-monitoring time series data, risking misinterpretation as kinetic phenomena of genuine chemical origin. Fitting first-order exponential decay models to the RGB sum response time series demonstrated that gamut clipping causes apparent rate constants to be underestimated. Our findings underscore the importance of paying close attention to colour representation in digital chemistry. We offer practical guidance for researchers using and interpreting colour data for use in computer vision method development.

PMID:
42438719
Bibliographic data and abstract were imported from PubMed on 13 Jul 2026.

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