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Facile and highly precise pH-value estimation using common pH paper based on machine learning techniques and

Mohamed M Elsenety¹, Mahmoud Basseem I Mohamed², Mohamed E Sultan²

¹Department of Chemistry, Faculty of Science, Al-Azhar University, Nasr City, Cairo, 11884, Egypt. m.elsenety@azhar.edu.eg.

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|December 30, 2022

Summary

Machine learning enhances common pH paper for accurate, low-cost pH measurement. A free mobile app uses RGB color data to predict pH values precisely, making advanced pH detection accessible to everyone.

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Area of Science:

Analytical Chemistry
Sensor Technology
Machine Learning Applications

Background:

Growing demand for cost-effective, high-precision optical pH sensors across scientific, healthcare, and industrial sectors.
Existing challenges in achieving both high accuracy and cost-effectiveness in pH sensor development.
Need for accessible pH measurement tools beyond expensive laboratory equipment.

Purpose of the Study:

To implement machine learning techniques for precise pH value estimation using standard pH paper.
To develop a user-friendly, free mobile application for accurate pH prediction from pH paper color.
To assess the feasibility of smartphone-based pH detection under varying light conditions.

Main Methods:

Utilized machine learning (K Neighbors Regressor) to analyze RGB color data from pH paper.
Trained models on 2689 experimental pH values across a wide pH range (1-14) with 0.1 pH intervals.
Investigated performance under different light intensities (350, 200, 20 Lux) without specialized instrument control.
Developed a free mobile application integrating the trained machine learning algorithm.

Main Results:

Identified significant correlations between pH values and red/green color components of pH paper, with weaker correlation for blue.
The K Neighbors Regressor model demonstrated excellent linearity and a high coefficient of determination (0.995).
Achieved precise pH estimation with minimal error, covering the full pH range.
Successfully validated the mobile application for accurate pH value prediction.

Conclusions:

Machine learning applied to common pH paper offers a highly accurate and cost-effective alternative to expensive pH instruments.
The developed free mobile application democratizes precise pH measurement, enabling widespread use by individuals and professionals.
Smartphone-based pH detection systems hold potential to replace traditional methods, offering convenience and accessibility.