Abstract
The consumption of antibiotics, such as sulfonamides, by humans and animals has increased in recent decades, and with it their presence in aquatic environments. This contribute to the increasing of bacterial resistant genes, making the treatment of infectious diseases more difficult. These antibiotics are usually detected by taking a water sample to a laboratory and quantifying it using expensive methods. Recently, digital colorimetry, has emerged as a new method for detecting sulfonamides in water. When a reagent comes into contact water sample containing sulfonamides, a color is produced from which we can infer the concentration of sulfonamides. To ensure that the color is not affected by the illumination when taking a photograph, a color reference target is positioned next to the sample to correct the colors.
This method has already been implemented in smartphones to provide a faster and more practical tool that can be used immediately when collecting water samples. Despite this improvement, the algorithms used can still be outperformed by the use of machine learning. In this work, we presented a machine learning approach and a mobile app to solve the problem of sulfonamides quantification. The machine learning approach was designed to run locally in the mobile device, while the mobile application is transversal to Android and iOS operation systems.
This work was partially funded by the Project TAMI - Transparent Artificial Medical Intelligence (NORTE-01-0247-FEDER-045905) financed by ERDF - European Regional Fund through the North Portugal Regional Operational Program - NORTE 2020 and by the Portuguese Foundation for Science and Technology - FCT under the CMU - Portugal International Partnership.
I. Rocha and F. Azevedo—These authors contributed equally.
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Rocha, I., Azevedo, F., Carvalho, P.H., Peixoto, P.S., Segundo, M.A., Oliveira, H.P. (2022). An Edge-Based Computer Vision Approach for Determination of Sulfonamides in Water. In: Pinho, A.J., Georgieva, P., Teixeira, L.F., Sánchez, J.A. (eds) Pattern Recognition and Image Analysis. IbPRIA 2022. Lecture Notes in Computer Science, vol 13256. Springer, Cham. https://doi.org/10.1007/978-3-031-04881-4_33
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