Abstract
The detection of Escherichia coli bacteria is essential to prevent health diseases. According to the laboratory-based methods, 12–48 h is required to detect bacteria in water. The drawback of depending on laboratory-based methods for the detection of E. coli bacteria can be prone to human errors. Hence, the bacterial detection process must be automated to reduce error. We implement an automated E. coli bacteria detection process using convolutional neural network (CNN) to address this issue. We have also proposed a mobile application for the rapid detection of E. coli bacteria in water that uses CNN. The developed CNN model achieved an accuracy of 96% and an error (loss) of 0.10, predicting each sample in only 458ms. The performance of the model was validated using the F-score, precision, sensitivity, and accuracy statistical measures, which shows that the model is reliable and effective in detecting E. coli. The study generates a methodology for predicting E. coli bacteria in water, which can be used to predict hotspots in terms of continuous exposure to water contamination.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Data availability
Some or all data, models, or code generated or used during the study are proprietary or confidential in nature and may only be provided with restrictions.
References
Abadi M, Barham P, Chen J, Chen Z, Davis A, Dean J, Zheng X (2016) Tensorflow: a system for large-scale machine learning. In: 12th {USENIX} symposium on operating systems design and implementation ({OSDI} 16), pp 265–283. Retrieved from https://www.usenix.org/conference/osdi16/technical-sessions/presentation/abadi
AI-driven test system detects bacteria in water (2018) Retrieved from https://software.intel.com/content/www/us/en/develop/articles/ai-driven-test-system-detects-bacteria-in-water.html
Alaslani, M. G. (2018) Convolutional neural network based feature extraction for iris recognition. International Journal of Computer Science & Information Technology (IJCSIT) Vol, 10
Alsing O (2018) Mobile object detection using tensorflow lite and transfer learning. Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-233775
BIS I (2012) 10500: 2012. Indian standard drinking water-specification (second revision), Bureau of Indian Standards, New Delhi
Brownlee J (2019) A gentle introduction to object recognition with deep learning. Machine Learning Mastery 5. Retrieved from https://machinelearningmastery.com/object-recognition-with-deep-learning/
Central Bureau of Health Intelligence (2018) National Health Profile 2018. Ministry of Health and Family Welfare. Government of India, New Delhi
Co-ordination Action Food (CAF) (2007). Methods for detection and molecular characterisation of pathogenic Escherichia coli. In: O’Sullivan J, Bolton DJ, Duffy G, Baylis C, Tozzoli R, Wasteson Y, Lofdahl S (eds.)
Dalianis H (2018) Evaluation metrics and evaluation. In: Clinical Text Mining. Springer, Cham. https://doi.org/10.1007/978-3-319-78503-5_6
Diarrhoeal disease. World Health Organization (2017). Available Online at http://www.who.int/mediacentre/factsheets/fs330/en
Edition F (2011) Guidelines for drinking-water quality. WHO chronicle, 38(4):104–108
Fausett LV (2006) Fundamentals of neural networks: architectures, algorithms and applications. Pearson Education India
Fukushima K, Miyake S, Ito T (1983) Neocognitron: a neural network model for a mechanism of visual pattern recognition. IEEE transactions on systems, man, and cybernetics 5:826–834
George I, Petit M, Servais P (2000) Use of enzymatic methods for rapid enumeration of coliforms in freshwaters. J Appl Microbiol 88(3):404–413
Greenwood D, Slack R, Peutherer J (2003) Escherichia. In: Medical microbiology, 16th edn. Churchill Livingstone, Edinburgh, pp 265–273
Gunda NSK, Gautam SH, Mitra SK (2019) Artificial intelligence based mobile application for water quality monitoring. J Electrochem Soc 166(9):B3031–B3035
Gupta A, Gupta A, Gupta R (2018) Power and area efficient intelligent hardware design for water quality applications. Sensors & Transducers 227(11):67–72
Huang L, Wu T (2018) Novel neural network application for bacterial colony classification. Theor Biol Med Model 15(1):1–16
Hubel DH, Wiesel TN (1968) Receptive fields and functional architecture of monkey striate cortex. J Physiol 195(1):215–243
Jagals P, Grabow WOK, Griesel M, Jagals C (2000) Evaluation of selected membrane filtration and most probable number methods for the enumeration of faecal coliforms, Escherichia coli and Enterococci in environmental waters. Quant Microbiol 2(2):129–140
Khan A, Sohail A, Zahoora U, Qureshi AS (2020) A survey of the recent architectures of deep convolutional neural networks. Artif Intell Rev 53(8):5455–5516
Maas MB, Perold WJ, Dicks LMT (2017) Biosensors for the detection of Escherichia coli. Water SA 43(4):707–721
MathWorks: Deep Network Designer Toolbox Release 2020b– MATLAB & Simulink – MathWorks India, available at, last access: 07th March 2021
McCulloch WS, Pitts W (1943) A logical calculus of the ideas immanent in nervous activity. Bulletin Math Biophys 5(4):115–133
Mohanty SP, Hughes DP, Salathé M (2016) Using deep learning for image-based plant disease detection. Front Plant Sci 7:1419
Nehal SA, Roy D, Devi M, Srinivas T (2019) Highly sensitive lab-on-chip with deep learning AI for detection of bacteria in water. Int J Inf Technol 12:495–501. https://doi.org/10.1007/s41870-019-00363-1
Olstadt J, Schauer JJ, Standridge J, Kluender S (2007) A comparison of ten USEPA approved total coliform/E. coli tests. J Water Health 5(2):267–282
Oshiro R (2002) Method 1604: total coliforms and Escherichia coli in water by membrane filtration using a simultaneous detection technique (MI Medium). Washington, DC: US Environmental Protection Agency
Rice EW, Geldreich EE, Read EJ (1989) The presence-absence coliform test for monitoring drinking water quality. Public Health Rep 104(1):54–58
Riley LW, Remis RS, Helgerson SD, McGee HB, Wells JG, Davis BR et al (1983) Hemorrhagic colitis associated with a rare Escherichia coli serotype. N Engl J Med 308(12):681–685
Saxena T, Kaushik P, Mohan MK (2015) Prevalence of E. coli O157: H7 in water sources: an overview on associated diseases, outbreaks and detection methods. Diagn Microbiol Infect Dis 82(3):249–264
Studio A (2017) Android studio. The Official IDE for Android
Tamerat N, Muktar Y, Shiferaw D (2016) Application of molecular diagnostic techniques for the detection of E. coli O157: H7: a review. J Vet Sci Technol 7(362):1–9
US EPA (2007) Drinking water standards and health advisories table. Edition of the Drinking Water Standards and Health Advisories
Zapata BC (2013) Android studio application development (p. 110). Packt Publishing
Acknowledgements
Project “Costs and Remediation of Groundwater Contamination” was funded by the Department of Economics, University of Virginia, and Global Program of Distinction Award (GPOD).
Author information
Authors and Affiliations
Contributions
Farhan Mohammad Khan: conceptualization; methodology; investigation; formal analysis; visualization; writing—original draft; and paper administration.
Rajiv Gupta: conceptualization, writing—review and editing.
Sheetal Sekhri: writing—review and editing.
Corresponding author
Ethics declarations
Ethics approval and consent to participate
Not applicable
Consent for publication
Not applicable
Competing interests
The authors declare no competing interests.
Additional information
Responsible Editor: Diane Purchase
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Khan, F.M., Gupta, R. & Sekhri, S. A convolutional neural network approach for detection of E. coli bacteria in water. Environ Sci Pollut Res 28, 60778–60786 (2021). https://doi.org/10.1007/s11356-021-14983-3
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11356-021-14983-3