Machine learning-enabled non-destructive paper chromogenic array detection of multiplexed viable pathogens on food

Manyun Yang, Xiaobo Liu, Yaguang Luo, Arne J. Pearlstein, Shilong Wang, Hayden Dillow, Kevin Reed, Zhen Jia, Arnav Sharma, Bin Zhou, Dan Pearlstein, Hengyong Yu, Boce Zhang

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish (US)
Pages (from-to)110-117
Number of pages8
JournalNature Food
Volume2
Issue number2
DOIs
StatePublished - Feb 2021

ASJC Scopus subject areas

  • Agronomy and Crop Science
  • Animal Science and Zoology
  • Food Science

Fingerprint

Dive into the research topics of 'Machine learning-enabled non-destructive paper chromogenic array detection of multiplexed viable pathogens on food'. Together they form a unique fingerprint.

Cite this