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A device-independent method for the colorimetric quantification on microfluidic sensors using a color adaptation

Junjie Feng1, Huiyun Jiang2, Yan Jin2

  • 1SINOPEC Research Institute of Safety Engineering Co., Ltd., State Key Laboratory of Safety and Control for Chemicals, 339 Songling Road, Qingdao, 266100, China. feng-jj@hotmail.com.

Mikrochimica Acta
|March 23, 2023
PubMed
Summary

A new method uses smartphone images to accurately quantify microfluidic sensor results. This technique corrects for lighting and device variations, improving measurement reliability for various colorimetric detection methods.

Keywords:
ColorimetryImage processingMicrofluidic sensorsPaper-based analytical devicesSmartphone

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Area of Science:

  • Analytical Chemistry
  • Biomedical Engineering
  • Image Processing

Background:

  • Colorimetric sensing is widely used for quantitative analysis.
  • Smartphone imaging offers a portable and accessible detection platform.
  • Variations in lighting, device, and user introduce significant errors in smartphone-based colorimetry.

Purpose of the Study:

  • To develop a general and adaptable method for reliable quantitative analysis from smartphone images of microfluidic sensors.
  • To reduce the influence of environmental and device-specific factors on image-based measurements.
  • To validate the method using a specific application in chromium ion quantification.

Main Methods:

  • Analyzing and processing color information from standard substances for chroma correction.
  • Utilizing machine learning and multivariate fitting for accurate color calibration.
  • Developing and validating a custom mobile application (APP) for data acquisition and analysis.
  • Employing a high-sensitivity chromium ion quantification paper chip for testing.

Main Results:

  • Significantly reduced influence of light conditions, device differences, and human factors.
  • Achieved over 75% reduction in average chroma deviations in RGB color space.
  • Reduced concentration test error by more than half compared to conventional methods.
  • Demonstrated the effectiveness of machine learning and multivariate fitting for chroma correction.

Conclusions:

  • The proposed method provides a reliable approach for quantitative analysis using smartphone images of microfluidic sensors.
  • The developed APP and calibration technique enhance measurement accuracy and reduce errors.
  • This adaptable method can supplement existing and future colorimetry-based detection techniques.