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Smartphone Based Colorimetric Detection via Machine Learning

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. Non-integer pH levels are identified as their nearest integer pH values, whereas test results for integer pH levels using 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.

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Publication details

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


Article type: Paper
DOI: 10.1039/C7AN00741H
Citation: Analyst, 2017, Accepted Manuscript
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    Smartphone Based Colorimetric Detection via Machine Learning

    A. Y. Mutlu, V. Kilic, G. Kocakusak, A. Bayram, N. Horzum and M. Solmaz, Analyst, 2017, Accepted Manuscript , DOI: 10.1039/C7AN00741H

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