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pH

The potential of hydrogen (pH) is a measure of the acidity or basicity of a water-based solution determined by the concentration of hydronium ions (H3O+). In one liter of pure water at neutral pH, there are 1×10−7 moles of hydronium ions. However, the extensive range of hydronium ion concentrations present in water-based solutions makes measuring pH in moles cumbersome. Therefore, a pH scale was developed to convert moles of hydronium ions into the negative logarithm of the hydronium ion...

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Fluorescent Paper Strips for the Detection of Diesel Adulteration with Smartphone Read-out

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High-accuracy machine learning-based colorimetric pH quantification using a custom-built portable strip-imaging

Ece Minel Bursalı¹, Mehmet Akif Özdemir¹, Mustafa Şen²

¹Department of Biomedical Engineering, Faculty of Engineering and Architecture, Izmir Katip Celebi University, Izmir, Turkey.

Mikrochimica Acta

|November 25, 2025

Summary

This summary is machine-generated.

A new machine learning-enhanced pH sensor platform offers accurate, real-time, offline pH monitoring. This portable device overcomes limitations of traditional strips and smartphone systems, ideal for resource-limited settings.

Keywords:

Colorimetric detection Embedded hardware Machine learning Smartphone application pH sensing

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Area of Science:

Analytical Chemistry
Sensor Technology
Machine Learning

Background:

Traditional pH strips suffer from subjective interpretation.
Smartphone pH sensing is affected by camera and lighting inconsistencies.
A need exists for accurate, portable, and reliable pH monitoring solutions.

Purpose of the Study:

To develop a compact, machine learning-enhanced pH sensing platform.
To overcome the limitations of existing pH measurement technologies.
To enable accurate, real-time, offline pH prediction.

Main Methods:

Developed a custom ESP32-S3 device with controlled LED illumination.
Collected a 787-sample dataset across the pH range of 0-14.
Extracted 45 statistical features from multiple color spaces (RGB, HSV, CIELAB).
Utilized an AutoML pipeline to select an ExtraTreesRegressor model.

Main Results:

Achieved a high coefficient of determination ([Formula: see text] = 0.967) with the ExtraTreesRegressor model.
Significantly outperformed traditional RGB-based methods ([Formula: see text] = 0.618).
Integrated Android application provides real-time, offline pH prediction via Wi-Fi.

Conclusions:

The developed platform offers a robust, accurate, and portable solution for pH monitoring.
Eliminates dependency on smartphone cameras and variable lighting conditions.
Well-suited for applications in resource-limited settings requiring precise pH measurements.