Project Number: 8042-42000-021-016-I
Project Type: Interagency Reimbursable Agreement
Start Date: Jun 1, 2023
End Date: Sep 30, 2024
Development and validation of rapid and nondestructive spectral sensing technologies and methodologies to provide user friendly tools for improving sanitation operations in food processing facilities, assessing food product safety, and monitoring crops in controlled-environment agriculture to better detect and mitigate early-stage health and safety problems in live food crops that can lead to increased crop loss or incidence of foodborne illnesses. Objectives are to (1) determine the efficacy of using portable sensing devices to detect potential presence of pathogens and biological agents on foods and processing equipment and decontaminate food contact surfaces in processing and production environments; and (2) develop an automated spectral imaging system to use in controlled-environment production of fresh product crops for monitoring the health of the plants and detecting contaminants, stress, or diseases to enable earlier-stage interventions to minimize losses in harvest yield.
In a cooperation between academic, industry, and government partners, the newly developed portable contamination and sanitation inspection devices will be evaluated for their efficacies in detecting contaminants on food products and food contact equipment surfaces and in implementing UVC-based decontamination treatment of food processing surfaces. In addition, multimodal imaging technologies and methodologies incorporating concomitant use of hyperspectral fluorescence and visible/near-infrared reflectance will be evaluated and identified to develop an automated imaging gantry system to be used in controlled-environment agriculture applications, such as but not limited to growth chambers used for space crop production, with the aim of developing a prototype monitoring system for routine preharvest assessment of health and safety issues for live crops that support can support early interventions to prevent or mitigate crop losses. Systems development will include algorithms for automation of imaging operations as well as data analysis, with methods incorporating artificial intelligence and machine learning.