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

• Published in: Frontiers in sensors Vol. 3
• Main 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., Qin, Jianwei, Baek, Insuck, Kim, Moon S., Akhbardeh, Alireza, Sohrabi, Mona, Kerge, Brick, MacKinnon, Nicholas, Vasefi, Fartash, Tavakolian, Kouhyar
• Format: Journal Article
• Language: English
• Published: Frontiers Media S.A 22.09.2022
• Subjects: contamination detection; deep learning; fluorescence imaging; food service industry; semantic segmentation
• ISSN: 2673-5067; 2673-5067
• DOI: 10.3389/fsens.2022.977770

Precise, reliable, and speedy contamination detection and disinfection is an ongoing challenge for the food-service industry. Contamination in food-related 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.