Jump to main content
Jump to site search

Issue 13, 2017
Previous Article Next Article

Smartphone-based colorimetric detection via machine learning

Author affiliations

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 conducted to study the effect of color change on the learning model. Non-integer pH levels are identified as their nearest integer pH values, whereas the test results for integer pH levels using JPEG, RAW and RAW-corrected image formats captured under different lighting conditions lead to perfect classification accuracy, sensitivity and specificity, which proves that 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.

Graphical abstract: Smartphone-based colorimetric detection via machine learning

Back to tab navigation

Publication details

The article was received on 04 May 2017, accepted on 14 May 2017 and first published on 19 May 2017


Article type: Paper
DOI: 10.1039/C7AN00741H
Citation: Analyst, 2017,142, 2434-2441
  •   Request permissions

    Smartphone-based colorimetric detection via machine learning

    A. Y. Mutlu, V. Kılıç, G. K. Özdemir, A. Bayram, N. Horzum and M. E. Solmaz, Analyst, 2017, 142, 2434
    DOI: 10.1039/C7AN00741H

Search articles by author

Spotlight

Advertisements