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

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.PMID:38183791 | DOI:10.1016/j.bios.2024.115999

Source: Biosensors and Bioelectronics - January 6, 2024 Category: Biotechnology Authors: 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 Source Type: research