Application of biosensing technology in the rapid identification of pathogenic microorganisms
Abstract
Background: Biosensing technology has developed as a capable tool for the rapid and perfect recognition of pathogenic microorganisms, determining real-time recognition capabilities that are crucial for early disease diagnosis and management. Purpose: This research investigates the integration of biosensors with Machine Learning (ML) techniques for the efficient detection of pathogens. Approaches: Data collection involved using various biosensors, including electrochemical, optical, and mass-based sensors, to capture the microbial signature of different pathogens. The collected data was pre-processed using adaptive filtering (AF) to remove noise and ensure signal clarity. Z-score normalization is utilized to standardize the dataset. Feature extraction was performed using the discrete wavelet transform (DWT) technique to reduce the dimensionality of the data while retaining crucial information. Results: This research proposes an Enhanced Snow Ablation Optimized Adaptive Support Vector Machine (ESAO-ASVM) model designed to enhance the accuracy and effectiveness of classifying complex biological data, such as microbial signatures. The proposed ESAO-ASVM model demonstrated optimal performance with an execution time of 0.58 s, memory usage of 42%, Root mean squared error (RMSE) of 0.01, Mean squared error (MSE) of 0.019, accuracy loss of 0.06, Structural similarity index measure (SSIM) of 80.4, accuracy of 98.5%, precision of 95%, recall of 94%, and an F1-score of 96%. This approach suggests a robust solution for rapidly identifying pathogenic microorganisms, making it an effective clinical diagnostic tool for food safety, and environmental monitoring. Conclusion: The incorporation of Biosensing technology with ML methods contains potential significant improvement in pathogen recognition, enabling faster and more consistent health involvements.
References
1. Abdellatif, S.O., Mosalam, H. and Hussien, S.A., 2024. Experimentally Verified Effective Doping Model for Lactate and Troponin OFET Biosensors using Machine Learning Algorithm. IEEE Transactions on Nanotechnology. https://doi.org/10.1109/TNANO.2024.3396505
2. Abdelrahman Ali, Y.A., Kamani, T., Patel, S.K., Armghan, A. and Almawgani, A.H., 2024. Design and Investigation of Machine Learning–Optimized Surface Plasmon Resonance Biosensor for Early Brain Tumor Detection. Plasmonics, pp.1-21. https://doi.org/10.1007/s11468-024-02635-4
3. Ardila, C.M., 2025. Advancing healthcare through laboratory on a chip technology: Transforming microorganism identification and diagnostics. World Journal of Clinical Cases, 13(3). https://dx.doi.org/10.12998/wjcc.v13.i3.97737
4. Baz, A., Wekalao, J., Mandela, N. and Patel, S.K., 2024. Design and performance evaluation of machine learning-based terahertz metasurface chemical sensor. IEEE Transactions on NanoBioscience. https://doi.org/10.1109/TNB.2024.3453372
5. Bhaiyya, M., Rewatkar, P., Pimpalkar, A., Jain, D., Srivastava, S.K., Zalke, J., Kalambe, J., Balpande, S., Kale, P., Kalantri, Y. and Kulkarni, M.B., 2024. Deep Learning-Assisted Smartphone-Based Electrochemiluminescence Visual Monitoring Biosensor: A Fully Integrated Portable Platform. Micromachines, 15(8), p.1059. https://doi.org/10.3390/mi15081059
6. Hadded, A., Ayed, M.B. and Alshaya, S.A., 2024. High Sensitivity and Specificity in Healthcare: Design and Validation of a Novel SiNW-FET Biosensor for Viral Detection. IEEE Access. https://doi.org/10.1109/ACCESS.2024.3442428
7. Hemdan, M., Ali, M.A., Doghish, A.S., Mageed, S.S.A., Elazab, I.M., Khalil, M.M., Mabrouk, M., Das, D.B. and Amin, A.S., 2024. Innovations in Biosensor Technologies for Healthcare Diagnostics and Therapeutic Drug Monitoring: Applications, Recent Progress, and Future Research Challenges. Sensors(Basel, Switzerland), 24(16), p.5143. https://doi.org/10.3390/s24165143
8. Jemmali, A., Kaziz, S., Echouchene, F. and Gazzah, M.H., 2024. Optimization of Lab-on-a CD by experimental design and machine learning models for microfluidic biosensor application. IEEE Sensors Journal.https://doi.org/10.1109/JSEN.2023.3343908
9. Li, Y., Cui, Z., Wang, Z., Shi, L., Zhuo, J., Yan, S., Ji, Y., Wang, Y., Zhang, D. and Wang, J., 2024. Machine-Learning-Assisted Aggregation-Induced Emissive Nanosilicon-Based Sensor Array for Point-of-Care Identification of Multiple Foodborne Pathogens. Analytical chemistry, 96(17), pp.6588-6598. https://pubs.acs.org/doi/10.1021/acs.analchem.3c05662?goto=supporting-info
10. Liu, X., Xie, Y., Yan, Y., Niu, Q., Zhu, L.G., Dong, Z., Liu, Q.H. and Zhu, J., 2024. Rapid On-Demand Design of Inverted All-Dielectric Metagratings for Trace Terahertz Molecular Fingerprint Sensing by Deep Learning. ACSPhotonics. https://pubs.acs.org/doi/10.1021/acsphotonics.4c01358?goto=supporting-info
11. Luo, X., Tan, H. and Wen, W., 2024. Recent Advances in Wearable Healthcare Devices: From Material to Application. Bioengineering, 11(4), p.358. https://doi.org/10.3390/bioengineering11040358
12. Quan, H., Wang, S., Xi, X., Zhang, Y., Ding, Y., Li, Y., Lin, J. and Liu, Y., 2024. Deep learning enhanced multiplex detection of viable foodborne pathogens in digital microfluidic chip. Biosensors and Bioelectronics, 245, p.115837. https://doi.org/10.1016/j.bios.2023.115837
13. Rane, N.L., Desai, P. and Choudhary, S., 2024. Challenges of implementing artificial intelligence for smart and sustainable industry: Technological, economic, and regulatory barriers. Artificial Intelligence and Industry in Society, 5, pp.2-83. https://doi.org/10.70593/978-81-981271-1-2_5
14. Sethia, D. and Indu, S., 2024. Optimization of wearable biosensor data for stress classification using machine learning and explainable AI. IEEE Access. https://doi.org/10.1109/ACCESS.2024.3463742
15. Shivakumar, N., 2024. Recent Advances in Biological Nanodevices and Biosensors: Insights into Applications and Technological Innovations. Malaysian NANO-An International Journal, 4(1), pp.86-101. https://doi.org/10.22452/mnij.vol4no1.6
16. Upadhyay, S., Kumar, A., Srivastava, M., Srivastava, A., Dwivedi, A., Singh, R.K. and Srivastava, S.K., 2024. Recent advancements of smartphone-based sensing technology for diagnosis, food safety analysis, and environmental monitoring. Talanta, p.126080. https://doi.org/10.1016/j.talanta.2024.126080
17. Wekalao, J. and Mandela, N., 2024. GrapheneMetasurface-Based Biosensor for Direct Dopamine Detection Utilizing Surface Plasmon Resonance in the Terahertz Regime with Machine Learning Optimization via K-Nearest Neighbors Regression. Plasmonics, pp.1-29. https://doi.org/10.1007/s11468-024-02570-4
18. Wekalao, J. and Mandela, N., 2024. Terahertz metasurface biosensor for high-sensitivity salinity detection and data encoding with machine learning optimization based on regression. Optical and Quantum Electronics, 56(11), p.1826. https://doi.org/10.1007/s11082-024-07777-7
19. Wekalao, J., Kumaresan, M.S., Mallan, S., Murthy, G.S., Nagarajan, N.R., Karthikeyan, S., Dorairajan, N., Prabu, R.T. and Rashed, A.N.Z., 2024. Metasurface Based Surface Plasmon Resonance (SPR) Biosensor for Cervical Cancer Detection with Behaviour Prediction using Machine Learning Optimization Based on Support Vector Regression. Plasmonics, pp.1-24. https://doi.org/10.1007/s11468-024-02623-8
20. Wekalao, J., Mandela, N., Langat, W. and wamalwa, C., 2024. Enhanced fuel adulteration detection using surface plasmon resonance biosensor with machine learning optimization in the terahertz regime. Plasmonics, pp.1-25. https://doi.org/10.1007/s11468-024-02550-8
21. Wekalao, J., Mandela, N., Lefu, C., Apochi, O., Wamalwa, C. and Langat, W., 2024. Terahertz plasmonic biosensor leveraging Ag-Au-grapheneheterostructures for quantitative hemoglobin analysis with machine learning algorithms for performance optimization. Plasmonics, pp.1-25. https://doi.org/10.1007/s11468-024-02520-0
22. Wekalao, J., Mandela, N., Obed, A. and Bouhenna, A., 2024. Design and evaluation of tunable terahertz metasurface biosensor for malaria detection with machine learning optimization using artificial intelligence. Plasmonics, pp.1-25. https://doi.org/10.1007/s11468-024-02491-2
23. Wekalao, J., Mandela, N., Selvam, A.K., Venugopal, S., Ravi, D., Pandian, P., Babu, A.J., Leon, M.L. and Rashed, A.N.Z., 2024. GrapheneMetasurface Based Biosensor for COVID-19 Detection in the Terahertz Regime with Machine Learning Optimization using K-Nearest Neighbours Regression. Plasmonics, pp.1-23. https://doi.org/10.1007/s11468-024-02686-7
24. Wekalao, J., Srinivasan, G.P., Patel, S.K. and Al-zahrani, F.A., 2025. Optimization of graphene-based biosensor design for haemoglobin detection using the gradient boosting algorithm for behaviour prediction. Measurement, 239, p.115452.https://doi.org/10.1016/j.measurement.2024.115452
25. Zhang, J., Srivatsa, P., Ahmadzai, F.H., Liu, Y., Song, X., Karpatne, A., Kong, Z.J. and Johnson, B.N., 2024. Improving biosensor accuracy and speed using dynamic signal change and theory-guided deep learning. Biosensors and Bioelectronics, 246, p.115829. https://doi.org/10.1016/j.bios.2023.115829
26. Dataset from Kaggle: https://www.kaggle.com/datasets/ziya07/biosensing-technology-for-pathogen-detection/data
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