Enhancing Smartphone Colorimetric Sensors via Color Space Optimization

Smartphone-based colorimetric (bio)sensors are a promising alternative for developing affordable, deliverable, and user-friendly analytical tests for healthcare, food safety, and environmental monitoring. However, their effectiveness is limited by sensitivity to lighting conditions, which frequently requires the use of housings with controlled light sources that compromise affordability and simplicity. This study introduces a novel framework for enhancing smartphone-based colorimetric sensing via color space optimization. This approach enables accurate and consistent measurements under varying lighting conditions without additional housing. We evaluated the performance of smartphone-based colorimetric models to quantify monotonal color gradients with spectral compositions covering a wide range of visible spectra. In addition, we benchmarked the smartphone-based colorimetric models against absorbance-based models built using a benchtop UV-Vis spectrophotometer. Our findings indicate that smartphone-based quantification can achieve accuracy, precision, and detection limits comparable to absorbance-based models while offering a broader dynamic range. By assessing the quantification performance across several color spaces—RGB, HSV, and CIELAB—we found that the a* and b* chromatic coordinates of CIELAB demonstrate exceptional resilience to changes in illumination. We introduce the concept of Equichromatic Surfaces as an innovative framework for understanding the illumination resilience of CIELAB. This concept serves as a guide for developing reliable, housing-free, illumination-invariant optical (bio)sensors.