The optimal color space enables advantageous smartphone-based colorimetric sensing

Area of Science:

• Analytical Chemistry
• Optical Sensing
• Biotechnology

Background:

• Smartphone colorimetric sensing offers portable on-site testing but suffers from lighting sensitivity.
• Conventional solutions use fixed light sources, increasing cost and complexity, contradicting on-site testing priorities.

Purpose of the Study:

• To demonstrate that optimized color space selection can achieve housing-free, illumination-invariant smartphone colorimetric sensing.
• To evaluate the performance and reliability of different color spaces for quantitative smartphone-based colorimetry.

Main Methods:

• Evaluated quantification performance using monotonal color shadings across the visible spectrum.
• Extracted color coordinates from automatically selected regions of interest (ROI) using a custom algorithm.
• Compared models based on RGB and CIELAB color spaces, focusing on chromatic coordinates (a* and b*).

Main Results:

• Smartphone colorimetry showed a broader measurement range and comparable limit of detection to absorbance-based models.
• RGB color space models were highly sensitive to illumination variations, impacting reliability.
• CIELAB color space (a* and b* coordinates) demonstrated inherent resistance to illumination changes.

Conclusions:

• Optimized color space selection, particularly CIELAB, enables robust, illumination-invariant smartphone colorimetric sensing.
• The concept of equichromatic surfaces provides a theoretical foundation for designing resilient optical (bio)sensors.
• This approach facilitates simpler, more affordable, and reliable on-site testing solutions.