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

Nature Food volume 2, pages 110–117 (2021)Cite this article

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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.

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Fig. 1: Schematic of multiplex detection of viable pathogens using a PCA coupled with machine learning and automated pattern recognition.
Fig. 2: PCA patterns from headspace exposure to VOCs from pathogens.
Fig. 3: PCA response to bacterial VOCs.
Fig. 4: Workflow of PCA fabrication, database construction and NN algorithm training.
Fig. 5: Machine learning of PCA patterns using a trained NN.
Fig. 6: Multiplex identification of viable pathogens on fresh-cut romaine lettuce in a simulated temperature-abuse scenario (12 °C for 7 days).

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Data availability

The datasets generated during and/or analysed during the current study are available from the corresponding authors on reasonable request. The sample dataset is available in the GitHub repository (https://github.com/FoodSafetyResearch/Nature-Food---Machine-Learning-Enabled-Paper-Chromogenic-Array.git).

Code availability

The NN algorithm is available in the GitHub repository (https://github.com/FoodSafetyResearch/Nature-Food---Machine-Learning-Enabled-Paper-Chromogenic-Array.git).

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Acknowledgements

The project is supported by the US Department of Agriculture (S51600000035794). We are grateful to Z. Teng, E. Turner and T. Gu for their assistance in PCA fabrication. The contents are solely the responsibility of the authors and do not necessarily represent the official views of the US Department of Agriculture.

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Author notes

  1. These authors contributed equally: Manyun Yang, Xiaobo Liu.

Authors and Affiliations

  1. Department of Biomedical and Nutritional Sciences, University of Massachusetts, Lowell, MA, USA

    Manyun Yang, Xiaobo Liu, Hayden Dillow, Kevin Reed, Zhen Jia & Boce Zhang

  2. Environmental Microbial and Food Safety Lab, US Department of Agriculture, Agriculture Research Service, Beltsville, MD, USA

    Yaguang Luo, Bin Zhou & Dan Pearlstein

  3. Department of Mechanical Science and Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, USA

    Arne J. Pearlstein

  4. Department of Electrical and Computer Engineering, University of Massachusetts, Lowell, MA, USA

    Shilong Wang & Hengyong Yu

  5. Department of Biological Sciences, University of Connecticut, Farmington, CT, USA

    Arnav Sharma

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Contributions

X.L., Y.L., H.Y. and B. Zhang designed the research; M.Y., X.L., S.W., H.D., K.R., Z.J., A.S. and B. Zhou performed the experiments and analysed the data; Y.L., D.P., A.J.P., H.Y. and B. Zhang wrote the paper.

Corresponding authors

Correspondence to Yaguang Luo or Boce Zhang.

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Competing interests

US provisional patent and patent cooperation treaty (PCT) applications have been filed (B. Zhang, Y.L., H.Y. and X.L. Method and system for chromogenic array-based food testing. US provisional patent application no. 62/757,388 and PCT/ US2019/059816).

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Peer review information Nature Food thanks Byron Brehm-Stecher, Hyeran Noh, Jasmina Vidic and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Yang, M., Liu, X., Luo, Y. et al. Machine learning-enabled non-destructive paper chromogenic array detection of multiplexed viable pathogens on food. Nat Food 2, 110–117 (2021). https://doi.org/10.1038/s43016-021-00229-5

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