SMART-DETECTION AND PREVENTIVE CONTROLS: EMERGENT TECHNOLOGIES FOR MICROBIAL FOOD SAFETY IN SOILLESS LEAFY GREENS SYSTEMS

Goal 1 [Research]: Assess and enhance procedures to detect Listeria monocytogenes contamination on leafy greens (LGs) in soilless systems and build capacity in customized next-generation pathogen detection Artificial Intelligence (AI) systems. We aim to expand our successful development of Listeria biosensors by interfacing them with a smartphone to facilitate rapid screening of Listeria in soilless systems. We will test the efficacy of tethered DNA aptamers and antibodies for targeting Internalin A (InlA), a Listeria-specific invasion protein. We will use established models of impedance data from our previous work in lettuce HP systems to investigate sensor behavior in 3 types (Nutrient Film Technique, Deep Water, and Media) of soilless systems. Three sites for sampling will be investigated to determine performance of swab sampling: 1) roots; 2) plant support media (rockwool or foam); 3) shoots (leaves). Beyond sensing, we will provide elements of statistical analysis by integrating support vector machine learning (SVML) to classify data using fog/edge analytics, as we have demonstrated in other projects.Goal 2 [Education]: Develop and deliver fresh produce microbial food safety learning opportunities for students on soilless LGs at UMES, as a model for other 1890 and land-grant institutions.Goal 3 [Extension/Outreach]: Provide technical and food safety practices guidance for soilless LGs production and preventive controls workshops and trainings for faculty, extension specialists, students, and growers.