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Food Res Int

Title:		Nondestructive and multiplex differentiation of pathogenic microorganisms from spoilage microflora on seafood using paper chromogenic array and neural network
Author(s):		Yang M; Luo Y; Sharma A; Jia Z; Wang S; Wang D; Lin S; Perreault W; Purohit S; Gu T; Dillow H; Liu X; Yu H; Zhang B;
Address:		"Department of Biomedical and Nutritional Sciences, University of Massachusetts, Lowell, MA 01854, United States; Department of Microbiology and Immunology, Cornell University, Ithaca, NY 14853, United States. Environmental Microbial and Food Safety Lab and Food Quality Lab, U.S. Department of Agriculture, Agricultural Research Service, Beltsville, MD 20705, United States. Department of Physiology and Neurobiology, University of Connecticut, Storrs, CT 06269, United States; Department of Electrical and Computer Engineering, University of Massachusetts, Lowell, MA 01854, United States. Department of Physiology and Neurobiology, University of Connecticut, Storrs, CT 06269, United States. Department of Food Science and Human Nutrition, University of Florida, 572 Newell Dr., Gainesville, FL 32611, United States. Department of Biomedical and Nutritional Sciences, University of Massachusetts, Lowell, MA 01854, United States. Department of Physiology and Neurobiology, University of Connecticut, Storrs, CT 06269, United States. Electronic address: boce.zhang@ufl.edu"
Journal Title:		Food Res Int
Year:		2022
Volume:		20221017
Issue:		Pt B
Page Number:		112052 -
DOI:		10.1016/j.foodres.2022.112052
ISSN/ISBN:		1873-7145 (Electronic) 0963-9969 (Linking)
Abstract:		"Non-destructive detection of human foodborne pathogens is critical to ensuring food safety and public health. Here, we report a new method using a paper chromogenic array coupled with a machine learning neural network (PCA-NN) to detect viable pathogens in the presence of background microflora and spoilage microbe in seafood via volatile organic compounds sensing. Morganella morganii and Shewanella putrefaciens were used as the model pathogen and spoilage bacteria. The study evaluated microbial detection in monoculture and cocktail multiplex detection. The accuracy of PCA-NN detection was first assessed on standard media and later validated on cod and salmon as real seafood models with pathogenic and spoilage bacteria, as well as background microflora. In this study PCA-NN method successfully identified pathogenic microorganisms from microflora with or without the prevalent spoilage microbe, Shewanella putrefaciens in seafood, with accuracies ranging from 90% to 99%. This approach has the potential to advance smart packaging by achieving nondestructive pathogen surveillance on food without enrichment, incubation, or other sample preparation"
Keywords:		"Humans *Neural Networks, Computer Machine Learning Food Safety Product Packaging Seafood *Shewanella putrefaciens Amine Neural network Paper chromogenic array Pathogen;"
Notes:		"MedlineYang, Manyun Luo, Yaguang Sharma, Arnav Jia, Zhen Wang, Shilong Wang, Dayang Lin, Sophia Perreault, Whitney Purohit, Sonia Gu, Tingting Dillow, Hyden Liu, Xiaobo Yu, Hengyong Zhang, Boce eng Research Support, U.S. Gov't, Non-P.H.S. Canada 2022/12/04 Food Res Int. 2022 Dec; 162(Pt B):112052. doi: 10.1016/j.foodres.2022.112052. Epub 2022 Oct 17"