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Machine learning-enabled non-destructive paper chromogenic array detection of multiplexed viable pathogens on food
- Source :
- Nature Food. 2:110-117
- Publication Year :
- 2021
- Publisher :
- Springer Science and Business Media LLC, 2021.
-
Abstract
- Fast and simultaneous identification of multiple viable pathogens on food is critical to public health. Here we report a pathogen identification system using a paper chromogenic array (PCA) enabled by machine learning. The PCA consists of a paper substrate impregnated with 23 chromogenic dyes and dye combinations, which undergo colour changes on exposure to volatile organic compounds emitted by pathogens of interest. These colour changes are digitized and used to train a multi-layer neural network (NN), endowing it with high-accuracy (91–95%) strain-specific pathogen identification and quantification capabilities. The trained PCA–NN system can distinguish between viable Escherichia coli, E. coli O157:H7 and other viable pathogens, and can simultaneously identify both E. coli O157:H7 and Listeria monocytogenes on fresh-cut romaine lettuce, which represents a realistic and complex environment. This approach has the potential to advance non-destructive pathogen detection and identification on food, without enrichment, culturing, incubation or other sample preparation steps. Fast and simultaneous identification of multiple viable pathogens on food is critical to public health. By integrating paper chromogenic arrays (PCAs) and machine learning, a system was developed to automatically recognize PCA patterns on multiplexed viable pathogens with strain-level specificity.
- Subjects :
- Pathogen detection
Chromogenic
business.industry
Computer science
medicine.disease_cause
Machine learning
computer.software_genre
Multiplexing
Identification system
Listeria monocytogenes
Non destructive
medicine
Animal Science and Zoology
Identification (biology)
Artificial intelligence
business
Agronomy and Crop Science
computer
Food Science
Subjects
Details
- ISSN :
- 26621355
- Volume :
- 2
- Database :
- OpenAIRE
- Journal :
- Nature Food
- Accession number :
- edsair.doi...........10b78c72d19541abc36bf924d4647c95