Journal of Sensor Science and Technology (센서학회지)

The Korean Sensors Society (한국센서학회)
권호 표지
DOI QR코드

DOI QR Code

Machine Vision Platform for High-Precision Detection of Disease VOC Biomarkers Using Colorimetric MOF-Based Gas Sensor Array

비색 MOF 가스센서 어레이 기반 고정밀 질환 VOCs 바이오마커 검출을 위한 머신비전 플랫폼

  • Junyeong Lee (Electronics and Ttelecommunications Research Institute, Digital Convergence Research Laboratory) ;
  • Seungyun Oh (Department of Chemistry, Chungnam National University) ;
  • Dongmin Kim (Department of Chemistry, Chungnam National University) ;
  • Young Wung Kim (WENS) ;
  • Jungseok Heo (Department of Chemistry, Chungnam National University) ;
  • Dae-Sik Lee (Electronics and Ttelecommunications Research Institute, Digital Convergence Research Laboratory)
  • 이준영 (한국전자통신연구원 진단치료기연구실) ;
  • 오승윤 (충남대학교 화학과) ;
  • 김동민 (충남대학교 화학과) ;
  • 김영웅 (웬스) ;
  • 허정석 (충남대학교 화학과) ;
  • 이대식 (한국전자통신연구원 진단치료기연구실)
  • Received : 2024.03.06
  • Accepted : 2024.03.25
  • Published : 2024.03.31
Download PDF
⟨ Previous Next ⟩

Abstract

Gas-sensor technology for volatile organic compounds (VOC) biomarker detection offers significant advantages for noninvasive diagnostics, including rapid response time and low operational costs, exhibiting promising potential for disease diagnosis. Colorimetric gas sensors, which enable intuitive analysis of gas concentrations through changes in color, present additional benefits for the development of personal diagnostic kits. However, the traditional method of visually monitoring these sensors can limit quantitative analysis and consistency in detection threshold evaluation, potentially affecting diagnostic accuracy. To address this, we developed a machine vision platform based on metal-organic framework (MOF) for colorimetric gas sensor arrays, designed to accurately detect disease-related VOC biomarkers. This platform integrates a CMOS camera module, gas chamber, and colorimetric MOF sensor jig to quantitatively assess color changes. A specialized machine vision algorithm accurately identifies the color-change Region of Interest (ROI) from the captured images and monitors the color trends. Performance evaluation was conducted through experiments using a platform with four types of low-concentration standard gases. A limit-of-detection (LoD) at 100 ppb level was observed. This approach significantly enhances the potential for non-invasive and accurate disease diagnosis by detecting low-concentration VOC biomarkers and offers a novel diagnostic tool.

Keywords

Acknowledgement

이 논문은 과학기술정보통신부의 재원으로 한국연구재단 나노 및 소재기술개발사업 (NRF-2021M3H4A4079271)의 지원을 받아 수행된 연구임.

References

  1. J. Pereira, P. Porto-Figueira, C. Cavaco, K. Taunk, S. Rapole, R. Dhakne, H. Nagarajaram, and J. S. Camara, "Breath analysis as a potential and non-invasive frontier in disease diagnosis: an overview", Metabolites, Vol. 5, No. 1, pp. 3-55, 2015. https://doi.org/10.3390/metabo5010003
  2. P. Mao, H. Li, and Z. Yu, "A Review of Skin-Wearable Sensors for Non-Invasive Health Monitoring Applications", Sensors, Vol. 23, No. 7, pp. 3673(1)-3673(37), 2023.
  3. R. Jana, S. Hajra, P. M. Rajaitha, K. Mistewicz, and H. J. Kim, "Recent advances in multifunctional materials for gas sensing applications", J. Environ. Chem. Eng., Vol. 10, No. 6, pp. 108543(1)-108543(24), 2022.
  4. T. Chen, T. Liu, T. Li, H. Zhao, and Q. Chen, "Exhaled breath analysis in disease detection", Clin. Chim. Acta, Vol. 515, No. 1, pp. 61-72, 2021. https://doi.org/10.1016/j.cca.2020.12.036
  5. K. Kawamura, K. Miyazawa, and L. Kent, "The Past, Present and Future in Tube- and Paper-Based Colorimetric Gas Detectors", Applied Chem., Vol. 1, No. 1, pp. 14-40, 2021. https://doi.org/10.3390/appliedchem1010003
  6. L. Yuan, M. Gao, H. Xiang, Z. Zhou, D. Yu, and R. Yan, "A Biomass-Based Colorimetric Sulfur Dioxide Gas Sensor for Smart Packaging", ACS Nano, Vol. 17, No. 7, pp. 6849-6856, 2023.
  7. J. Yu, D. Wang, V. V. Tipparaju, F. Tsow, and X. Xian, "Mitigation of Humidity Interference in Colorimetric Sensing of Gases", ACS Sens., Vol. 6, No. 2, pp. 303-320, 2021. https://doi.org/10.1021/acssensors.0c01644
  8. J. Roales, F. G. Moscoso, A. P. Vargas, T. Lopes-Costa, and J. M. Pedrosa, "Colorimetric Gas Detection Using Molecular Devices and an RGB Sensor", Chemosensors, Vol. 11, No. 2, pp. 92(1)-92(13), 2023.
  9. H. Wang, T. Wu, M. Li, and Y. Tao, "Recent advances in nanomaterials for colorimetric cancer detection", J. Mater. Chem. B, Vol. 9, No. 4, pp. 921-938, 2021. https://doi.org/10.1039/D0TB02163F