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Electrochemical Immunosensor in Combination with an Artificial Neural Network Study for Pathogenic Bacteria Detection using a Modified Glassy Carbon Electrode

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Abstract

We report here the results of studies related to the fabrication of an electrochemical immunosensor for the detection of Escherichia coli ATCC 25922 using AuNPs-GCE-avidin-Ab-E. coli based on complex compound. In the presence of targeted bacteria, the specific antibody was coated on the surface with gold nanoparticles (AuNPs). The detailed morphology of synthesized AuNPs was confirmed using analytical techniques. The proposed immunosensor revealed a high electrocatalytic activity and linear response at the peak potential value over a wide concentration of E. coli ATCC 25922 from 101 to 105 CFU/mL. The results were correlated with the linear equation of (R2 = 0.991). The recorded results were responded in the presence of targeted E. coli ATCC 25922 with other bacterial strains such as Salmonella typhi, Klebsiella aerogenes, and E. coli O57:H7 indicating a high selectivity of electrochemical immunosensor. A combined artificial neural network (ANN) approach serves as a powerful model to understand and analyze the intelligent data of the digital transformation output. The determined regression method of the fabricated sensor was selected for evaluation of the ANN-based technique that initiated to be a superior known method. The applied technique confirmed a great practical approach to targeted bacteria in spiked samples of the sandwich complex. Therefore, the satisfactory result demonstrates the feature of simulation data attainment and analysis is highly reliable and attractive. Moreover, the constructed immunosensor may be used to screen contaminated water and prevents an epidemic of life-threatening infectious disease.

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ACKNOWLEDGMENTS

Dr. H.A. Keerio is grateful to Higher Education Commission (HEC) Pakistan for providing the placement at environment department, QUEST, Nawabshah.

Funding

To the core of our heart and thanks from all authors to Scientific and Technological Research Council (TUBITAK), Türkiye, who supported us with funding (E-21514107-115.02-345503) for the 2221 visiting scientist programme.

Author information

Authors and Affiliations

  1. Department of Civil Engineering, National University of Science, and Technology, Balochistan Campus, Quetta, Pakistan

    S. Panhwar

  2. Gazi University, Faculty of Pharmacy, Department of Analytical Chemistry, Ankara, Turkey

    S. Panhwar

  3. Department of Environmental Engineering, Quaid E Awam University of Engineering Science and Technology, 67450, Nawabshah, Pakistan

    H. A. Keerio

  4. Faculty of Engineering & Quantity Surveying, INTI International University, Persiaran Perdana BBN, Putra Nilai, 1800, Nilai, Negeri Sembilan, Malaysia

    H. A. Keerio

  5. Department of Civil Engineering Technology, Benazir Bhutto Shaheed University of Technology & Skill. Development, 66020, Khairpur, Pakistan

    A. Ali

  6. Department of Science and Technology, Civil Engineering Technology Program, Indus University, 75300, Karachi, Pakistan

    N. H. Khokhar

  7. Department of Chemical and Energy Engineering, Pak-Austria Fachhochschule: Institute of Applied Sciences and Technology, Mang, Haripur, Pakistan

    M. Muqeet

  8. Department of Civil Engineering, Mehran UET, 66020, Khairpur Mirs, Campus, Sindh, Pakistan

    G. S. Solangi

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Correspondence to S. Panhwar or H. A. Keerio.

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Panhwar, S., Keerio, H.A., Ali, A. et al. Electrochemical Immunosensor in Combination with an Artificial Neural Network Study for Pathogenic Bacteria Detection using a Modified Glassy Carbon Electrode. Appl Biochem Microbiol 59, 975–985 (2023). https://doi.org/10.1134/S0003683823060261

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