TY - JOUR
T1 - Surveillance of pathogenic bacteria on a food matrix using machine-learning-enabled paper chromogenic arrays
AU - Jia, Zhen
AU - Luo, Yaguang
AU - Wang, Dayang
AU - Holliday, Emma
AU - Sharma, Arnav
AU - Green, Madison M.
AU - Roche, Michelle R.
AU - Thompson-Witrick, Katherine
AU - Flock, Genevieve
AU - Pearlstein, Arne J.
AU - Yu, Hengyong
AU - Zhang, Boce
N1 - Publisher Copyright:
© 2024 Elsevier B.V.
PY - 2024/3/15
Y1 - 2024/3/15
N2 - 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.
AB - 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.
KW - Detection
KW - Machine learning
KW - Paper chromogenic array sensor
KW - Pathogenic bacteria
UR - http://www.scopus.com/inward/record.url?scp=85181814740&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85181814740&partnerID=8YFLogxK
U2 - 10.1016/j.bios.2024.115999
DO - 10.1016/j.bios.2024.115999
M3 - Article
C2 - 38183791
AN - SCOPUS:85181814740
SN - 0956-5663
VL - 248
JO - Biosensors and Bioelectronics
JF - Biosensors and Bioelectronics
M1 - 115999
ER -