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Smartphone-based colorimetric analysis of pH strips using machine learning.

Ece Yıldız1, Mustafa Şen1, Mehmet Akif Özdemir1,2

  • 1Department of Biomedical Engineering, Izmir Katip Celebi University, Izmir, Turkiye. makif.ozdemir@ikcu.edu.tr.

Analytical Methods : Advancing Methods and Applications
|June 16, 2026
PubMed
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A new smartphone app uses machine learning (ML) to accurately measure pH using colorimetric pH strips. This low-cost, offline tool offers reliable pH quantification, especially for resource-limited settings.

Area of Science:

  • Analytical Chemistry
  • Computational Science

Background:

  • Accurate pH measurement is crucial across various scientific disciplines.
  • Traditional pH strips often lack precision and are susceptible to environmental factors.
  • Developing accessible, low-cost pH quantification methods is essential for broader applications.

Purpose of the Study:

  • To develop a machine learning-enhanced smartphone application for precise colorimetric quantification of pH strips.
  • To create a robust system that accounts for variations in illumination and camera angles.
  • To integrate a user-friendly, offline pH analysis tool for resource-limited environments.

Main Methods:

  • Image acquisition of pH strips under diverse conditions.
  • Extraction of colorimetric features and training of regression models.

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  • SHapley Additive exPlanations (SHAP) analysis for feature selection and model interpretability.

    • Main Results:

    • Identification of six critical colorimetric descriptors for pH prediction.
    • Development of a high-performance regression model (R² = 0.99).
    • Integration into the Android application 'pHScoper' for on-device, offline analysis.

    Conclusions:

    • The pHScoper application provides reliable, low-cost pH measurements.
    • The ML-enhanced approach ensures robustness against environmental variations.
    • This technology holds significant potential for accessible pH monitoring in resource-limited settings.