Journal of the Korean Society of Visualization (한국가시화정보학회지)

The Korean Society of Visualization (한국가시화정보학회)
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Deep Neural Network Technology for Analyzing PDA Colorimetric Transition Sensors in Pathogen Detection

병원균 검출용 PDA 색 전이 센서 분석을 위한 심층신경망 기술

  • Junhyeon Jeon (Department of Mechanical Engineering, Inha University) ;
  • Huisoo Jang (Industrial Science and Technology Research Institute, Inha University) ;
  • Mingyeong Shin (Department of Food and Nutrition, Inha University) ;
  • Tae-Joon Jeon (Department of Biological Engineering, Department of Biological Sciences and Bioengineering, Biohybrid Systems Research Center, Inha University) ;
  • Sun Min Kim (Department of Mechanical Engineering, Department of Biological Sciences and Bioengineering, Biohybrid Systems Research Center, Inha University)
  • Received : 2024.04.11
  • Accepted : 2024.05.19
  • Published : 2024.07.31
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Abstract

In this study, we propose a novel approach for rapid and accurate pathogen detection by integrating Polydiacetylene (PDA) hydrogel sensors with advanced deep learning algorithms and visualization techniques. PDA hydrogel sensors exhibit a color transition in the presence of pathogens, enabling straightforward and quick pathogen detection. We developed a reliable pathogen detection system that combines deep neural network algorithms with color quantification technology for image-based analysis. This image-based system retains the ease of pathogen detection offered by PDA sensors while deriving quantified color standards to overcome the limitations of human visual assessment, enhancing reliability. This advancement contributes to public health and the development and application of pathogen detection technology.

Keywords

Acknowledgement

이 성과는 정부(과학기술정보통신부)의 재원으로 한국연구재단의 지원을 받아 수행된 연구임(NRF-2021R1A2C2003571, RS-2023-00207801).

References

  1. Aycicek, H., Aydogan, H., Kucukkaraaslan, A., Baysallar, M., and Basustaoglu, A. C., 2004, "Assessment of the Bacterial Contamination on Hands of Hospital Food Handlers," Food Control, Vol. 15(4), pp. 253-259. 
  2. De Boer, E., and Beumer, R. R., 1999, "Methodology for Detection and Typing of Foodborne Microorganisms," International Journal Food Microbiology, Vol. 50(1-2), pp. 119-130. 
  3. Toze, S., 1999, "PCR and the Detection of Microbial Pathogens in Water and Wastewater," Water Research, Vol. 33(17), pp. 3545-3556. 
  4. Perry, L., Heard, P., Kane, M., Kim, H., Savikhin, S., Dominguez, W., and Applegate, B., 2007, "Application of Multiplex Polymerase Chain Reaction to the Detection of Pathogens in Food," Journal of Rapid Methods Autom Microbiology, Vol. 15(2), pp. 176-198. 
  5. Desai, P. T., Walsh, M. K., and Weimer, B. C., 2008, "Solid-Phase Capture of Pathogenic Bacteria by Using Gangliosides and Detection with Real-Time PCR," Applied and Environmental Microbiology, Vol. 74(7), pp. 2254-2258. 
  6. Cesewski, E., and Johnson, B. N., 2020, "Electrochemical Biosensors for Pathogen Detection," Biosensors and Bioelectronics, Vol. 159(1), p. 112214. 
  7. Yoo, S. M., and Lee, S. Y., 2016, "Optical Biosensors for the Detection of Pathogenic Microorganisms," Trends in Biotechnology, Vol. 34(1), pp. 7-25. 
  8. Silbert, L., Ben Shlush, I., Israel, E., Porgador, A., Kolusheva, S., and Jelinek, R., 2006, "Rapid Chromatic Detection of Bacteria by Use of a New Biomimetic Polymer Sensor," Applied and Environmental Microbiology, Vol. 72(11), pp. 7339-7344. 
  9. Song, S., Jang, H., Lee, D., Jeong, W., Bae, E. H., Kim, H., Choi, Y. S., Shin, M., Kim, S. M., and Jeon, T. J., 2023, "Portable Colorimetric Hydrogel Beads for Point-of-Care Antimicrobial Susceptibility Testing," ACS Sensors, Vol. 8(10), pp. 3754-3761. 
  10. Scindia, Y., Silbert, L., Volinsky, R., Kolusheva, S., and Jelinek, R., 2007, "Colorimetric Detection and Fingerprinting of Bacteria by Glass-Supported Lipid/Polydiacetylene Films," Langmuir, Vol. 23(8), pp. 4682-4687. 
  11. Nagy, J. O., Zhang, Y., Yi, W., Liu, X., Motari, E., Song, J. C., Lejeune, J. T., and Wang, P. G., 2008, "Glycopolydiacetylene Nanoparticles as a Chromatic Biosensor to Detect Shiga-like Toxin Producing Escherichia Coli O157:H7," Bioorganic and Medicinal Chemistry Letters, Vol. 18(2), pp. 700-703. 
  12. Siribunbandal, P., Kim, Y. H., Osotchan, T., Zhu, Z., and Jaisutti, R., 2022, "Quantitative Colorimetric Detection of Dissolved Ammonia Using Polydiacetylene Sensors Enabled by Machine Learning Classifiers," ACS Omega, Vol. 7(22), pp. 18714-18721. 
  13. Wang, X., Zhang, R., Kong, T., Li, L., and Shen, C., 2020, "SOLOv2: Dynamic and Fast Instance Segmentation," Advances in Neural Information Processing Systems, Vol. 33(2020), pp.17721-17732. 
  14. Pratt, W. K., 2002, "DIGITAL IMAGE PROCESSING: PIKS Inside", WILEY-Interscience, New York, pp. 753-757. 
  15. Weston, M., Kuchel, R. P., Ciftci, M., Boyer, C., and Chandrawati, R., 2020, "A Polydiacetylene-Based Colorimetric Sensor as an Active Use-by Date Indicator for Milk," Journal of Colloid and Interface Science, Vol. 572, pp. 31-38. 
  16. Pires, A. C. D. S., Soares, N. D. F. F., Da Silva, L. H. M., Da Silva, M. D. C. H., De Almeida, M. V., Le Hyaric, M., Andrade, N. J. De, Soares, R. F., Mageste, A. B., and Reis, S. G., 2011, "A Colorimetric Biosensor for the Detection of Foodborne Bacteria," Sensors and Actuators B Chemical, Vol. 153(1), pp. 17-23. 
  17. Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollar, P., and Zitnick, C. L., 2014, "Microsoft COCO: Common Objects in Context," The European Conference in Computer Vision, Vol. 8693, pp.740-755.