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Biosens Bioelectron

Title:		Nondestructive multiplex detection of foodborne pathogens with background microflora and symbiosis using a paper chromogenic array and advanced neural network
Author(s):		Jia Z; Luo Y; Wang D; Dinh QN; Lin S; Sharma A; Block EM; Yang M; Gu T; Pearlstein AJ; Yu H; Zhang B;
Address:		"Department of Biomedical and Nutritional Sciences, University of Massachusetts, Lowell, 01854, MA, USA. Environmental Microbial and Food Safety Lab and Food Quality Lab, U.S. Department of Agriculture, Agricultural Research Service, Beltsville, 20705, MD, USA. Department of Electrical and Computer Engineering, University of Massachusetts, Lowell, 01854, MA, USA. Department of Physiology and Neurobiology, University of Connecticut, Storrs, 06269, CT, USA. Department of Mechanical Science and Engineering, University of Illinois at Urbana-Champaign, Urbana, 61801, IL, USA. Department of Biomedical and Nutritional Sciences, University of Massachusetts, Lowell, 01854, MA, USA. Electronic address: boce_zhang@uml.edu"
Journal Title:		Biosens Bioelectron
Year:		2021
Volume:		20210401
Issue:
Page Number:		113209 -
DOI:		10.1016/j.bios.2021.113209
ISSN/ISBN:		1873-4235 (Electronic) 0956-5663 (Linking)
Abstract:		"We have developed an inexpensive, standardized paper chromogenic array (PCA) integrated with a machine learning approach to accurately identify single pathogens (Listeria monocytogenes, Salmonella Enteritidis, or Escherichia coli O157:H7) or multiple pathogens (either in multiple monocultures, or in a single cocktail culture), in the presence of background microflora on food. Cantaloupe, a commodity with significant volatile organic compound (VOC) emission and large diverse populations of background microflora, was used as the model food. The PCA was fabricated from a paper microarray via photolithography and paper microfluidics, into which 22 chromogenic dye spots were infused and to which three red/green/blue color-standard dots were taped. When exposed to VOCs emitted by pathogens of interest, dye spots exhibited distinguishable color changes and pattern shifts, which were automatically segmented and digitized into a DeltaR/DeltaG/DeltaB database. We developed an advanced deep feedforward neural network with a learning rate scheduler, L(2) regularization, and shortcut connections. After training on the DeltaR/DeltaG/DeltaB database, the network demonstrated excellent performance in identifying pathogens in single monocultures, multiple monocultures, and in cocktail culture, and in distinguishing them from the background signal on cantaloupe, providing accuracy of up to 93% and 91% under ambient and refrigerated conditions, respectively. With its combination of speed, reliability, portability, and low cost, this nondestructive approach holds great potential to significantly advance culture-free pathogen detection and identification on food, and is readily extendable to other food commodities with complex microflora"
Keywords:		"*Biosensing Techniques Colony Count, Microbial Food Microbiology *Listeria monocytogenes Neural Networks, Computer Reproducibility of Results Symbiosis Machine learning Paper chromogenic array Photolithography Viable bacteria detection Volatile organic co;"
Notes:		"MedlineJia, Zhen Luo, Yaguang Wang, Dayang Dinh, Quynh N Lin, Sophia Sharma, Arnav Block, Ethan M Yang, Manyun Gu, Tingting Pearlstein, Arne J Yu, Hengyong Zhang, Boce eng England 2021/04/10 Biosens Bioelectron. 2021 Jul 1; 183:113209. doi: 10.1016/j.bios.2021.113209. Epub 2021 Apr 1"