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Bacterial species differentiation via real-time detection of microbial volatile organic compounds using a wavelength multiplexed photoionization detector and AI image-based analysis
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  • Published: 03 April 2026

Bacterial species differentiation via real-time detection of microbial volatile organic compounds using a wavelength multiplexed photoionization detector and AI image-based analysis

  • Susana P. Costa1 na1,
  • António Cardoso2 na1,
  • Hedieh Mahmoodnia1,
  • Fábio Gonçalves1,
  • Adelaide Miranda1,
  • Felipe Yamada3,
  • Luís Guimarães3,
  • Flávia Barbosa4 &
  • …
  • Pieter De Beule1 

Scientific Reports , Article number:  (2026) Cite this article

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We are providing an unedited version of this manuscript to give early access to its findings. Before final publication, the manuscript will undergo further editing. Please note there may be errors present which affect the content, and all legal disclaimers apply.

Subjects

  • Computational biology and bioinformatics
  • Diseases
  • Microbiology

Abstract

Healthcare-associated infections (HCAIs) contribute significantly to global mortality, driven by the increasing antimicrobial resistance. Rapid, high-throughput bacterial detection is crucial for infection control and patient care. We report a real-time, multiplex lamp-based Photoionization Detector (PID) assisted by AI-image-based analysis for bacterial identification. Using four lamps with varying ionization energies, the sensor selectively ionizes VOCs emitted by bacteria, producing four distinct current curves for each target species (Escherichia coli, Staphylococcus aureus, Pseudomonas aeruginosa, and Klebsiella pneumoniae). These curves were transformed into image representations, capturing their spectral patterns for bacterial differentiation. A pre-trained ResNet-18 Convolutional Neural Network (CNN) within a Few-Shot Learning (FSL) framework extracted key features, enabling accurate (> 88%) bacterial differentiation even with limited labeled data. This sensor detected bacterial concentrations as low as 10² CFU and distinguished contamination levels. The synergistic integration of PID sensing with AI-driven analysis offers a powerful approach to rapid bacterial diagnostics, demonstrating strong potential for clinical implementation and improved patient care. This study marks an early step toward AI-based VOC sensing, where FSL acts as a proof-of-concept under data scarcity.

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Data availability

All processed data are available in the main text or the supplementary materials.

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Funding

The authors acknowledge the financial support of the project SMARTgNOSTICS, with the reference n.º C644915155-00000024, co-funded by Component C5 – Capitalisation and Business Innovation under the Portuguese Resilience and Recovery Plan, through the NextGenerationEU Fund.

Author information

Author notes

  1. These authors contributed equally to this work: Susana P. Costa and António Cardoso.

Authors and Affiliations

  1. International Iberian Nanotechnology Laboratory, Avenida Mestre José Veiga s/n, Braga, 4715-330, Portugal

    Susana P. Costa, Hedieh Mahmoodnia, Fábio Gonçalves, Adelaide Miranda & Pieter De Beule

  2. INESC TEC, Rua Dr. Roberto Frias, Porto, 4200-465, Portugal

    António Cardoso

  3. Faculdade de Engenharia, INESC TEC, Universidade do Porto, Rua Dr. Roberto Frias, Porto, 4200-465, Portugal

    Felipe Yamada & Luís Guimarães

  4. Faculdade de Economia, INESC TEC, Universidade do Porto, Rua Dr. Roberto Frias, Porto, 4200-464, Portugal

    Flávia Barbosa

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S.P.C., H.M., and F.G. designed and performed the experimental work. A.C. and F.Y. performed algorithm development; S.P.C., A.C., and F.Y. analyzed the data. S.P.C., H.M., and A.C. wrote the manuscript. All authors read, reviewed, and approved the final manuscript.

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Correspondence to Flávia Barbosa or Pieter De Beule.

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Costa, S.P., Cardoso, A., Mahmoodnia, H. et al. Bacterial species differentiation via real-time detection of microbial volatile organic compounds using a wavelength multiplexed photoionization detector and AI image-based analysis. Sci Rep (2026). https://doi.org/10.1038/s41598-026-46818-x

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  • Received: 07 November 2025

  • Accepted: 27 March 2026

  • Published: 03 April 2026

  • DOI: https://doi.org/10.1038/s41598-026-46818-x

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Keywords

  • Healthcare-associated infections
  • Portable bacterial detection tool
  • Photoionization detector
  • Volatile organic compounds
  • Convolutional neural network
  • Few-shot learning
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