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An Automated Toolchain for Camera-Enabled Sensing of Drinking Water Chlorine Residual

[Image: see text] Chlorine residual concentration is an important parameter to prevent pathogen growth in drinking water. Disposable color changing test strips that measure chlorine in tap water are commercially available to the public; however, the color changes are difficult to read by eye, and th...

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Autores principales: Schubert, Alyssa, Pifer, Leah, Cheng, Jianzhong, McElmurry, Shawn P., Kerkez, Branko, Love, Nancy G.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Chemical Society 2022
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9469768/
https://www.ncbi.nlm.nih.gov/pubmed/36120115
http://dx.doi.org/10.1021/acsestengg.2c00073
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author Schubert, Alyssa
Pifer, Leah
Cheng, Jianzhong
McElmurry, Shawn P.
Kerkez, Branko
Love, Nancy G.
author_facet Schubert, Alyssa
Pifer, Leah
Cheng, Jianzhong
McElmurry, Shawn P.
Kerkez, Branko
Love, Nancy G.
author_sort Schubert, Alyssa
collection PubMed
description [Image: see text] Chlorine residual concentration is an important parameter to prevent pathogen growth in drinking water. Disposable color changing test strips that measure chlorine in tap water are commercially available to the public; however, the color changes are difficult to read by eye, and the data are not captured for water service providers. Here we present an automated toolchain designed to process digital images of free chlorine residual test strips taken with mobile phone cameras. The toolchain crops the image using image processing algorithms that isolate the areas relevant for analysis and automatically white balances the image to allow for use with different phones and lighting conditions. The average red, green, and blue (RGB) color values of the image are used to predict a free chlorine concentration that is classified into three concentration tiers (<0.2 mg/L, 0.2–0.5 mg/L, or >0.5 mg/L), which can be reported to water users and recorded for utility use. The proposed approach was applied to three different phone types under three different lighting conditions using a standard background. This approach can discriminate between concentrations above and below 0.5 mg/L with an accuracy of 90% and 94% for training and testing data sets, respectively. Furthermore, it can discriminate between concentrations of <0.2 mg/L, 0.2–0.5 mg/L, or >0.5 mg/L with weighted-averaged F1 scores of 79% and 88% for training and testing data sets, respectively. This tool sets the stage for tap water consumers and water utilities to gather frequent measurements and high-resolution temporal and spatial data on drinking water quality.
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spelling pubmed-94697682023-06-03 An Automated Toolchain for Camera-Enabled Sensing of Drinking Water Chlorine Residual Schubert, Alyssa Pifer, Leah Cheng, Jianzhong McElmurry, Shawn P. Kerkez, Branko Love, Nancy G. ACS ES T Eng [Image: see text] Chlorine residual concentration is an important parameter to prevent pathogen growth in drinking water. Disposable color changing test strips that measure chlorine in tap water are commercially available to the public; however, the color changes are difficult to read by eye, and the data are not captured for water service providers. Here we present an automated toolchain designed to process digital images of free chlorine residual test strips taken with mobile phone cameras. The toolchain crops the image using image processing algorithms that isolate the areas relevant for analysis and automatically white balances the image to allow for use with different phones and lighting conditions. The average red, green, and blue (RGB) color values of the image are used to predict a free chlorine concentration that is classified into three concentration tiers (<0.2 mg/L, 0.2–0.5 mg/L, or >0.5 mg/L), which can be reported to water users and recorded for utility use. The proposed approach was applied to three different phone types under three different lighting conditions using a standard background. This approach can discriminate between concentrations above and below 0.5 mg/L with an accuracy of 90% and 94% for training and testing data sets, respectively. Furthermore, it can discriminate between concentrations of <0.2 mg/L, 0.2–0.5 mg/L, or >0.5 mg/L with weighted-averaged F1 scores of 79% and 88% for training and testing data sets, respectively. This tool sets the stage for tap water consumers and water utilities to gather frequent measurements and high-resolution temporal and spatial data on drinking water quality. American Chemical Society 2022-06-03 2022-09-09 /pmc/articles/PMC9469768/ /pubmed/36120115 http://dx.doi.org/10.1021/acsestengg.2c00073 Text en © 2022 The Authors. Published by American Chemical Society https://creativecommons.org/licenses/by/4.0/Permits the broadest form of re-use including for commercial purposes, provided that author attribution and integrity are maintained (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Schubert, Alyssa
Pifer, Leah
Cheng, Jianzhong
McElmurry, Shawn P.
Kerkez, Branko
Love, Nancy G.
An Automated Toolchain for Camera-Enabled Sensing of Drinking Water Chlorine Residual
title An Automated Toolchain for Camera-Enabled Sensing of Drinking Water Chlorine Residual
title_full An Automated Toolchain for Camera-Enabled Sensing of Drinking Water Chlorine Residual
title_fullStr An Automated Toolchain for Camera-Enabled Sensing of Drinking Water Chlorine Residual
title_full_unstemmed An Automated Toolchain for Camera-Enabled Sensing of Drinking Water Chlorine Residual
title_short An Automated Toolchain for Camera-Enabled Sensing of Drinking Water Chlorine Residual
title_sort automated toolchain for camera-enabled sensing of drinking water chlorine residual
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9469768/
https://www.ncbi.nlm.nih.gov/pubmed/36120115
http://dx.doi.org/10.1021/acsestengg.2c00073
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