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Multi-Wavelength Machine Learning for High-Precision Colorimetric Sensing
Majid Aalizadeh1,2, Chinmay Raut3, Ali Tabartehfarahani1,2,4,5
1Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI 48109, USA.
Sensors (Basel, Switzerland)
|June 12, 2026
Summary
Full-spectrum analysis significantly enhances colorimetric sensing accuracy. By using selected spectral features and linear models, researchers achieved over a 5700-fold improvement in predicting concentrations, outperforming single-wavelength methods.
Area of Science:
- Analytical Chemistry
- Spectroscopy
- Chemometrics
Background:
- Conventional colorimetric sensing often uses single wavelengths, ignoring rich spectral data.
- Heuristic wavelength selection can limit predictive accuracy in intensity-based systems.
Purpose of the Study:
- To demonstrate improved predictive accuracy in colorimetric sensing using full-spectrum data.
- To validate a feature selection strategy combined with linear regression for concentration prediction.
Main Methods:
- Applied forward feature selection to normalized transmission spectra.
- Utilized linear regression with ten-fold cross-validation.
- Tested the approach on food dye dilutions as a model system.
Main Results:
- Reduced mean squared error from over 22,000 (single wavelength) to 3.87 (twelve selected features).
- Achieved a >5700-fold enhancement in predictive accuracy.
- Validated the effectiveness of full-spectrum modeling without hardware changes.
Conclusions:
- Full-spectrum modeling offers a powerful, hardware-independent approach for precise colorimetric analysis.
- The demonstrated framework can be extended to diverse applications like medical diagnostics and environmental monitoring.
- Further validation with real analytes and complex matrices is recommended.
