Quantifying colorimetric tests using a smartphone app based on machine learning classifiers

• Published in: Sensors and actuators. B, Chemical Chemical, 2018-02, Vol.255, p.1967-1973
• Main Authors: Solmaz, Mehmet E., Mutlu, Ali Y., Alankus, Gazihan, Kılıç, Volkan, Bayram, Abdullah, Horzum, Nesrin
• Format: Article
• Language: English
• Subjects: Algorithms; Android application; Artificial intelligence; Change detection; Classifiers; Color; Colorimetry; Hydrogen peroxide; Illumination; Image classification; Image processing; Machine learning; Organic chemistry; Smartphone; Smartphones; Software utilities; Studies
• ISSN: 0925-4005; 1873-3077
• DOI: 10.1016/j.snb.2017.08.220

[Display omitted] •The ‘ChemTrainer’ smartphone application was developed for colorimetric detection.•Hydrogen peroxide strip images were used to train for machine learning classifiers.•Over 90% classification accuracy was obtained for primary peroxide levels.•Color constancy algorithms positively affected classification accuracy. 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.