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A Randomized Combined Channel Approach for the Quantification of Color- and Intensity-Based Assays with Smartphones.

Joost L D Nelis1, Yunfeng Zhao1,2, Laszlo Bura3

  • 1Institute for Global Food Security, School of Biological Sciences, Queen's University of Belfast, 19 Chlorine Gardens, Belfast BT9 5DL, United Kingdom.

Analytical Chemistry
|May 9, 2020
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Summary

Smartphone colorimetric assays can be improved using novel channel combinations. This study found that nonclassical combinations outperform standard color spaces for accurate quantification in lateral flow and pH assays.

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Area of Science:

  • Analytical Chemistry
  • Spectroscopy
  • Biotechnology

Background:

  • Smartphone-based colorimetric assays are increasingly common for quantification.
  • Existing color spaces lack complete characterization for optimal assay performance.
  • The necessity of using established color spaces for optimal quantification is unverified.

Purpose of the Study:

  • To investigate novel color channel combinations for enhanced smartphone colorimetric assay quantification.
  • To determine if nonclassical channel combinations can outperform standard RGB, HSV, and CieLab spaces.
  • To develop and validate a smartphone application for automated assay analysis.

Main Methods:

  • A randomized channel approach was employed, testing single, double, and triple channel combinations from RGB, HSV, and CieLab color spaces.
  • Assays involving pH strips and nanoparticle-containing paper strips were optimized using this approach.
  • A custom smartphone application was developed for automated background subtraction and polynomial regression.

Main Results:

  • Several novel channel combinations demonstrated superior performance, reducing prediction error and interphone variation compared to standard color spaces.
  • For a lateral flow assay detecting goat milk adulteration, the BSA channel combination yielded optimal results (MAE = 36% ± 6%, R² = 0.97).
  • For soil pH prediction, the RLC channel combination was optimal (MAE = 1.31% ± 0.02%, R² = 0.997).

Conclusions:

  • Nonclassical channel combinations offer significant promise for improving colorimetric quantification in smartphone-based analyses.
  • The findings suggest that exploring novel channel combinations is crucial for advancing the accuracy and reliability of mobile sensing technologies.
  • This approach can be applied to various assays, including food adulteration detection and environmental monitoring.