Smartphone Based Colorimetric Detection via Machine Learning

TL;DR

This paper demonstrates a machine learning approach using LS-SVM classifiers to accurately determine pH levels from smartphone images of colorimetric strips, robust across different image formats and lighting conditions.

Contribution

It introduces a machine learning method that effectively classifies pH values from smartphone images, accommodating various image formats and lighting variations.

Findings

Achieved perfect classification accuracy, sensitivity, and specificity.

Performance was unaffected by different image formats and lighting conditions.

Proves the robustness of machine learning in smartphone-based colorimetric sensing.

Abstract

We report the application of machine learning to smartphone based colorimetric detection of pH values. The strip images were used as the training set for Least Squares-Support Vector Machine (LS-SVM) classifier algorithms that were able to successfully classify the distinct pH values. The difference in the obtained image formats was found not to significantly affect the performance of the proposed machine learning approach. Moreover, the influence of the illumination conditions on the perceived color of pH strips was investigated and further experiments were carried out to study effect of color change on the learning model. Test results on JPEG, RAW and RAW-corrected image formats captured in different lighting conditions lead to perfect classification accuracy, sensitivity and specificity, which proves that the colorimetric detection using machine learning based systems is able to adapt to various experimental conditions and is a great candidate for smartphone based sensing in paper-based colorimetric assays.