| Title: | Machine learning-enabled non-destructive paper chromogenic array detection of multiplexed viable pathogens on food |
| Author(s): | Yang M; Liu X; Luo Y; Pearlstein AJ; Wang S; Dillow H; Reed K; Jia Z; Sharma A; Zhou B; Pearlstein D; Yu H; Zhang B; |
| Address: | "Department of Biomedical and Nutritional Sciences, University of Massachusetts, Lowell, MA, USA. Environmental Microbial and Food Safety Lab, US Department of Agriculture, Agriculture Research Service, Beltsville, MD, USA. yaguang.luo@usda.gov. Department of Mechanical Science and Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, USA. Department of Electrical and Computer Engineering, University of Massachusetts, Lowell, MA, USA. Department of Biological Sciences, University of Connecticut, Farmington, CT, USA. Environmental Microbial and Food Safety Lab, US Department of Agriculture, Agriculture Research Service, Beltsville, MD, USA. Department of Biomedical and Nutritional Sciences, University of Massachusetts, Lowell, MA, USA. boce_zhang@uml.edu" |
| DOI: | 10.1038/s43016-021-00229-5 |
| ISSN/ISBN: | 2662-1355 (Electronic) 2662-1355 (Linking) |
| 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" |
| Notes: | "PubMed-not-MEDLINEYang, Manyun Liu, Xiaobo Luo, Yaguang Pearlstein, Arne J Wang, Shilong Dillow, Hayden Reed, Kevin Jia, Zhen Sharma, Arnav Zhou, Bin Pearlstein, Dan Yu, Hengyong Zhang, Boce eng S51600000035794/U.S. Department of Agriculture ( Department of Agriculture)/ England 2021/02/01 Nat Food. 2021 Feb; 2(2):110-117. doi: 10.1038/s43016-021-00229-5. Epub 2021 Feb 18" |
