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Deep learning and multiwavelength fluorescence imaging for cleanliness assessment and disinfection in Food Services.

Authors :
Gorji, Hamed Taheri
Van Kessel, Jo Ann S.
Haley, Bradd J.
Husarik, Kaylee
Sonnier, Jakeitha
Shahabi, Seyed Mojtaba
Zadeh, Hossein Kashani
Chan, Diane E.
Jianwei Qin
Baek, Insuck
Kim, Moon S.
Akhbardeh, Alireza
Sohrabi, Mona
Kerge, Brick
MacKinnon, Nicholas
Vasefi, Fartash
Tavakolian, Kouhyar
Source :
Frontiers in Sensors (2673-5067); 9/22/2022, p1-14, 14p
Publication Year :
2022

Abstract

Precise, reliable, and speedy contamination detection and disinfection is an ongoing challenge for the food-service industry. Contamination in foodrelated services can cause foodborne illness, endangering customers and jeopardizing provider reputations. Fluorescence imaging has been shown to be capable of identifying organic residues and biofilms that can host pathogens. We use new fluorescence imaging technology, applying Xception and DeepLabv3+ deep learning algorithms to identify and segment contaminated areas in images of equipment and surfaces. Deep learning models demonstrated a 98.78% accuracy for differentiation between clean and contaminated frames on various surfaces and resulted in an intersection over union (IoU) score of 95.13% for the segmentation of contamination. The portable imaging system's intrinsic disinfection capability was evaluated on S. enterica, E. coli, and L. monocytogenes, resulting in up to 8-log reductions in under 5 s. Results showed that fluorescence imaging with deep learning algorithms could help assure safety and cleanliness in the food-service industry. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
26735067
Database :
Complementary Index
Journal :
Frontiers in Sensors (2673-5067)
Publication Type :
Academic Journal
Accession number :
174369128
Full Text :
https://doi.org/10.3389/fsens.2022.977770