Browsing by Author "Kilic, Volkan"

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Citation - WoS: 111
Citation - Scopus: 130
Quantifying Colorimetric Tests Using a Smartphone App Based on Machine Learning Classifiers
(Elsevier Science Sa, 2018) Solmaz, Mehmet E.; Mutlu, Ali Y.; Alankus, Gazihan; Kilic, Volkan; Bayram, Abdullah; Horzum, Nesrin
A smartphone application based on machine learning classifier algorithms was developed for quantifying peroxide content on colorimetric test strips. The strip images were taken from five different Android based smartphones under seven different illumination conditions to train binary and multi-class classifiers and to extract the learning model. A custom app, ChemTrainer, was designed to capture, crop, and process the active region of the strip, and then to communicate with a remote server that contains the learning model through a Cloud hosted service. The application was able to detect the color change in peroxide strips with over 90% success rate for primary colors with inter-phone repeatability under versatile illumination. The utilization of a grey-world color constancy image processing algorithm positively affected the classification accuracy for binary classifiers. The developed app with a Cloud based learning model paves the way for better colorimetric detection for paper-based chemical assays. (C) 2017 Elsevier B.V. All rights reserved.
Citation - WoS: 57
Citation - Scopus: 74
Single-Image Colorimetric Water Quality Detection Using a Smartphone
(Amer Chemical Soc, 2018) Kilic, Volkan; Alankus, Gazihan; Horzum, Nesrin; Mutlu, Ali Y.; Bayram, Abdullah; Solmaz, Mehmet E.
In this paper, we present a smartphone platform for colorimetric water quality detection based on the use of built-in camera for capturing a single-use reference image. A custom-developed app processes this image for training and creates a reference model to be used later in real experimental conditions to calculate the concentration of the unknown solution. This platform has been tested on four different water quality colorimetric assays with various concentration levels, and results show that the presented platform provides approximately 100% accuracy for colorimetric assays with noticeable color difference. This portable, cost-effective, and user-friendly platform is promising for application in water quality monitoring.