Surveillance of pathogenic bacteria on a food matrix using machine-learning-enabled paper chromogenic arrays

Zhen Jia, Yaguang Luo, Dayang Wang, Emma Holliday, Arnav Sharma, Madison M. Green, Michelle R. Roche, Katherine Thompson-Witrick, Genevieve Flock, Arne J. Pearlstein, Hengyong Yu, Boce Zhang

Research output: Contribution to journalArticlepeer-review


Global food systems can benefit significantly from continuous monitoring of microbial food safety, a task for which tedious operations, destructive sampling, and the inability to monitor multiple pathogens remain challenging. This study reports significant improvements to a paper chromogenic array sensor - machine learning (PCA-ML) methodology sensing concentrations of volatile organic compounds (VOCs) emitted on a species-specific basis by pathogens by streamlining dye selection, sensor fabrication, database construction, and machine learning and validation. This approach enables noncontact, time-dependent, simultaneous monitoring of multiple pathogens (Listeria monocytogenes, Salmonella, and E. coli O157:H7) at levels as low as 1 log CFU/g with over 90% accuracy. The report provides theoretical and practical frameworks demonstrating that chromogenic response, including limits of detection, depends on time integrals of VOC concentrations. The paper also discusses the potential for implementing PCA-ML in the food supply chain for different food matrices and pathogens, with species- and strain-specific identification.

Original languageEnglish (US)
Article number115999
JournalBiosensors and Bioelectronics
StatePublished - Mar 15 2024
Externally publishedYes


  • Detection
  • Machine learning
  • Paper chromogenic array sensor
  • Pathogenic bacteria

ASJC Scopus subject areas

  • Biophysics
  • Biotechnology
  • Biomedical Engineering
  • Electrochemistry


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