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Updated: Sep 11, 2025

A Colorimetric Method for Measuring Iron Content in Plants
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New Imaging Method of Mobile Phone-Based Colorimetric Sensor for Iron Quantification.

Ngan Anh Nguyen1,2, Asher Hendricks1,2, Emily Montoya1

  • 1School of Engineering for Matter, Transport and Energy, Arizona State University, Tempe, AZ 85281, USA.

Sensors (Basel, Switzerland)
|August 14, 2025
PubMed
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This summary is machine-generated.

This study presents an improved smartphone imaging method for accurate blood iron level measurement. The enhanced technique ensures reliable results despite varying light and camera conditions, aiding iron disorder diagnosis.

Area of Science:

  • Biomedical Engineering
  • Analytical Chemistry
  • Point-of-Care Diagnostics

Background:

  • Iron deficiency and overload affect millions globally, necessitating accessible diagnostic tools.
  • Previous research established a low-cost, point-of-care finger-prick blood test for iron measurement.
  • Variations in environmental illumination and camera quality previously impacted test accuracy.

Purpose of the Study:

  • To develop an improved imaging method for accurate iron detection using smartphone cameras.
  • To overcome limitations of environmental illumination and camera variability in sensor readings.
  • To enhance the reliability and accessibility of point-of-care iron level monitoring.

Main Methods:

  • Utilized smartphone cameras as analytical instruments for sensor image acquisition.
Keywords:
biosensorschemical sensorsimage color analysisiron detectionpoint-of-care sensorsmartphone-based detectionvertical flow assay

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  • Developed an image processing technique to compensate for varying lighting conditions.
  • Validated the method across different smartphone models to assess robustness.

    • Main Results:

    • Achieved an average coefficient of variation of 5.13% across various smartphone models.
    • Demonstrated an 8.80% improvement in absorbance measurement accuracy compared to prior methods.
    • Confirmed enhanced iron detection accuracy under diverse environmental lighting.

    Conclusions:

    • The improved imaging method significantly enhances the accuracy of smartphone-based iron detection.
    • This approach offers a robust and accessible solution for point-of-care diagnostics.
    • The technology holds potential for adapting smartphone sensing to other colorimetric assays.