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ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Environmental Microbial & Food Safety Laboratory » Research » Publications at this Location » Publication #370053

Research Project: Characterization and Mitigation of Bacterial Pathogens in the Fresh Produce Production and Processing Continuum

Location: Environmental Microbial & Food Safety Laboratory

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

item LIU, XIAOBAO - University Of Massachusetts
item YANG, MANYUN - University Of Massachusetts
item Luo, Yaguang - Sunny
item WANG, SHILONG - University Of Massachusetts
item Zhou, Bin
item TENG, ZI - US Department Of Agriculture (USDA)
item DILLOW, HAYDEN - University Of Massachusetts
item GU, TINGTING - University Of Massachusetts
item REED, KEVIN - University Of Massachusetts
item SHARM, ARNAV - University Of Massachusetts
item JIA, ZHEN - University Of Massachusetts
item YU, HENGYONG - University Of Massachusetts
item ZHANG, BOCE - University Of Massachusetts

Submitted to: Nature Food
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 1/18/2021
Publication Date: 2/18/2021
Citation: Liu, X., Yang, M., Luo, Y., Wang, S., Zhou, B., Teng, Z., Dillow, H., Gu, T., Reed, K., Sharm, A., Jia, Z., Yu, H., Zhang, B. 2021. Machine learning-enabled non-destructive paper chromogenic array detection of multiplexed viable pathogens on food. Nature Food. 2:110-117.

Interpretive Summary: The presence of harmful bacteria on food products is a major cause for food-borne illness outbreaks and the associated loss in human lives and the negative impact on the financial wellbeing of the food industry. Early detection and subsequent removal of contaminated foods from the supply chain is a critical measure to ensure food safety. In this study, we report a novel pathogen detection technology using a machine learning empowered paper chromogenic array capable of detecting and differentiating different pathogens with high accuracy. This research benefits pathogen detection method developers and the food industry.

Technical Abstract: Reliable pathogen detection in food is critical to the protection of public health by preventing food-borne illness outbreaks. Here we report a novel technology to detect viable human pathogens in complex food matrices using a paper chromogenic array (PCA) empowered by machine learning. The PCA was developed by impregnating a paper substrate with 5-23 chromogenic dyes, in which exposure to the characteristic volatile organic compounds (VOCs) of the target microorganisms elicits color changes. These color changes were digitized, and used to train a multi-layer Neural Network (NN), giving it strain-specific pathogen detection and quantification abilities with 91-95% accuracy. The trained PCA-NN system was capable of detecting and differentiating between viable Escherichia coli and Escherichia coli O157:H7 in fresh cut lettuce, a realistic and chemically complex environment. Overall, this machine learning empowered PCA technology represents a great potential tool for continuous, accurate, and non-destructive detection of microbial contamination in food.