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Peer-reviewedCC BY 4.0Microbiology Spectrum

Rapid identification of porcine bacterial pathogens using an artificial intelligence Raman microbial analyzer for biology

  • Chia-Hung Lu1
  • Ni-Jyun Ke2,3,4
  • Chi-Chun Liu2
  • Yu-Hui Liao1
  • Chu Wang1
  • Shih-Wei Liu1
  • Wei-Cheng Chang1
  • Chun-Yi Hsu1
  • Nai-Shun Liao1
  • Yi-Fan Chou1
  • Po-Ren Hsueh5,6,7,8
  • Ming-Tang Chiou2,3,4
1 more authors
  • Chao-Nan Lin2,3,4
8 affiliations
  1. 1ITRUST MedTech Inc, Hsinchu, Taiwan
  2. 2Department of Veterinary Medicine, College of Veterinary Medicine, National Pingtung University of Science and Technology, Pingtung, Taiwan
  3. 3Animal Disease Diagnostic Center, College of Veterinary Medicine, National Pingtung University of Science and Technology, Pingtung, Taiwan
  4. 4Sustainable Swine Research Center, National Pingtung University of Science and Technology, Pingtung, Taiwan
  5. 5Department of Laboratory Medicine, China Medical University Hospital, School of Medicine, China Medical University, Taichung, Taiwan
  6. 6Division of Infectious Diseases, Department of Internal Medicine, China Medical University Hospital, China Medical University, Taichung, Taiwan
  7. 7Department of Laboratory Medicine, National Taiwan University Hospital, National Taiwan University College of Medicine, Taipei, Taiwan
  8. 8Department of Internal Medicine, National Taiwan University Hospital, National Taiwan University College of Medicine, Taipei, Taiwan
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Abstract

Rapid and accurate identification of bacterial pathogens is critical for effective disease control in pig farms. Conventional bacterial culture-based and molecular diagnostic methods are time-consuming and often impractical for clinical decision-making. Raman spectroscopy, combined with artificial intelligence, offers a label-free alternative and provides isolate-level identification within approximately 2 min. The aim of this study was to evaluate the performance of the commercial Artificial Intelligent Raman Microbial Analyzer for Biology (AIRMAB) for the rapid clinical identification of swine bacterial pathogens. Bacterial isolates were collected from various tissues of diseased pigs between March and November 2025 at the Animal Disease Diagnostic Center, National Pingtung University of Science and Technology, Taiwan. The species-level identification of all the isolates was confirmed using species-specific polymerase chain reaction assays prior to Raman spectral acquisition, ensuring the accuracy of the reference labels. Overall, the AIRMAB system exhibited high performance in the identification of all 14 clinically relevant pig bacterial pathogens. Notably, perfect recall (100%) was achieved for clinically common species in the test set, including Pasteurella multocida (n = 30) and Salmonella enterica serovar Choleraesuis (n = 20). In addition, high recall (>90%) was observed for other important bacteria in the test set, including Escherichia coli (97%) (n = 33), Streptococcus suis (94%) (n = 35), and Glaesserella parasuis (93%) (n = 30). This study reports that the AIRMAB system provides rapid, accurate, and label-free identification of swine bacterial pathogens, delivering results within 2 min per isolate, and demonstrating practical value for timely decision-making in routine veterinary diagnostics, especially for common, and clinically important bacterial species.

IMPORTANCE: Bacterial infections pose a major challenge to global swine production, leading to growth retardation, elevated mortality, and substantial production losses. As several swine pathogens are zoonotic, they also pose direct and indirect risks to human health. Traditional diagnostic methods such as polymerase chain reaction are reliable but time-consuming and require well-equipped laboratories. This study introduces the Artificial Intelligent Raman Microbial Analyzer for Biology system, which utilizes Raman spectroscopy and deep learning to identify 14 major swine pathogens in approximately 2 min. This technology offers a highly automated, cost-effective alternative to traditional methods and matrix-assisted laser desorption ionization-time of flight mass spectrometry, facilitating rapid clinical intervention and large-scale screening to safeguard both animal and public health.

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Cite

BibTeXRIS
BibTeX
@article{lu2026rapid,
  title = {{Rapid identification of porcine bacterial pathogens using an artificial intelligence Raman microbial analyzer for biology}},
  author = {Chia-Hung Lu and Ni-Jyun Ke and Chi-Chun Liu and Yu-Hui Liao and Chu Wang and Shih-Wei Liu and Wei-Cheng Chang and Chun-Yi Hsu and Nai-Shun Liao and Yi-Fan Chou and Po-Ren Hsueh and Ming-Tang Chiou and Chao-Nan Lin},
  journal = {Microbiology Spectrum},
  year = {2026},
  month = oct,
  doi = {10.1128/spectrum.01792-26},
  url = {https://doi.org/10.1128/spectrum.01792-26},
  note = {CC BY 4.0},
}
RIS
TY  - JOUR
TI  - Rapid identification of porcine bacterial pathogens using an artificial intelligence Raman microbial analyzer for biology
AU  - Chia-Hung Lu
AU  - Ni-Jyun Ke
AU  - Chi-Chun Liu
AU  - Yu-Hui Liao
AU  - Chu Wang
AU  - Shih-Wei Liu
AU  - Wei-Cheng Chang
AU  - Chun-Yi Hsu
AU  - Nai-Shun Liao
AU  - Yi-Fan Chou
AU  - Po-Ren Hsueh
AU  - Ming-Tang Chiou
AU  - Chao-Nan Lin
PY  - 2026
DA  - 2026/10/06
JO  - Microbiology Spectrum
DO  - 10.1128/spectrum.01792-26
UR  - https://doi.org/10.1128/spectrum.01792-26
AB  - Rapid and accurate identification of bacterial pathogens is critical for effective disease control in pig farms. Conventional bacterial culture-based and molecular diagnostic methods are time-consuming and often impractical for clinical decision-making. Raman spectroscopy, combined with artificial intelligence, offers a label-free alternative and provides isolate-level identification within approximately 2 min. The aim of this study was to evaluate the performance of the commercial Artificial Intelligent Raman Microbial Analyzer for Biology (AIRMAB) for the rapid clinical identification of swine bacterial pathogens. Bacterial isolates were collected from various tissues of diseased pigs between March and November 2025 at the Animal Disease Diagnostic Center, National Pingtung University of Science and Technology, Taiwan. The species-level identification of all the isolates was confirmed using species-specific polymerase chain reaction assays prior to Raman spectral acquisition, ensuring the accuracy of the reference labels. Overall, the AIRMAB system exhibited high performance in the identification of all 14 clinically relevant pig bacterial pathogens. Notably, perfect recall (100%) was achieved for clinically common species in the test set, including Pasteurella multocida (n = 30) and Salmonella enterica serovar Choleraesuis (n = 20). In addition, high recall (>90%) was observed for other important bacteria in the test set, including Escherichia coli (97%) (n = 33), Streptococcus suis (94%) (n = 35), and Glaesserella parasuis (93%) (n = 30). This study reports that the AIRMAB system provides rapid, accurate, and label-free identification of swine bacterial pathogens, delivering results within 2 min per isolate, and demonstrating practical value for timely decision-making in routine veterinary diagnostics, especially for common, and clinically important bacterial species.  IMPORTANCE: Bacterial infections pose a major challenge to global swine production, leading to growth retardation, elevated mortality, and substantial production losses. As several swine pathogens are zoonotic, they also pose direct and indirect risks to human health. Traditional diagnostic methods such as polymerase chain reaction are reliable but time-consuming and require well-equipped laboratories. This study introduces the Artificial Intelligent Raman Microbial Analyzer for Biology system, which utilizes Raman spectroscopy and deep learning to identify 14 major swine pathogens in approximately 2 min. This technology offers a highly automated, cost-effective alternative to traditional methods and matrix-assisted laser desorption ionization-time of flight mass spectrometry, facilitating rapid clinical intervention and large-scale screening to safeguard both animal and public health.
SN  - 2165-0497
ER  -