Machine learning-enabled non-destructive paper chromogenic array detection of multiplexed viable pathogens on food
Manyun Yang(University of Massachusetts Lowell), Boce Zhang(University of Massachusetts Lowell), Kevin Reed(University of Massachusetts Lowell), Arne J. Pearlstein(University of Illinois Urbana-Champaign), Bin Zhou(United States Department of Agriculture), Arnav Sharma(University of Connecticut), Hengyong Yu(University of Massachusetts Lowell), Hayden Dillow(University of Massachusetts Lowell), Xiaobo Liu(University of Massachusetts Lowell), Zhen Jia(Auburn University), Yaguang Luo(United States Department of Agriculture), Shilong Wang(University of Massachusetts Lowell), Dan Pearlstein(United States Department of Agriculture)
Cited by 149
Related Papers
Intracellular signaling pathways of inflammation modulated by dietary flavonoids: The most recent evidence
|Critical Reviews in Food Science and Nutrition|2017|199
Nondestructive multiplex detection of foodborne pathogens with background microflora and symbiosis using a paper chromogenic array and advanced neural network
|Biosensors and Bioelectronics|2021|44
Surveillance of pathogenic bacteria on a food matrix using machine-learning-enabled paper chromogenic arrays
|Biosensors and Bioelectronics|2024|30
Listeria monocytogenes biofilm formation as affected by stainless steel surface topography and coating composition
|Food Control|2021|28
Hypolipidemic and anti‐atherogenic activities of crude polysaccharides from abalone viscera
|Food Science & Nutrition|2020|26