Recipient Organization
University of Maryland Eastern Shore
11868 College Backborne Road
Princess Anne,MD 21853
Performing Department
(N/A)
Non Technical Summary
Food safety practices and rapid, low-cost pathogen detection technologies are needed to detect contamination of leafy greens (LGs) in soilless systems. This integrated project will develop effective/efficient procedures to rapidly detect Listeria monocytogenes (Lm) pre-/post-harvest in soilless systems using a novel aptameric-nanosensor. Potential Lm detection interferents will be resolved by iterative assay adaptations. Nanosensor detection performance will be validated using standard methods. Comparison of system factors (nutrient solution quality, plant roots/shoots, and microbiome quality) from controlled research and soilless LGs producer systems will be used to reveal key factors contributing to growth/survival and Lm detection interferents. Using machine learning, we will adapt/modify the nanosensor and smart-phone technologies to develop a cost-effective, rapid Listeria monocytogenes detection system for use by producers. Dose challenge inoculation and microbial community trials will reveal relative contamination risks and effects of preventive controls on pathogens and maintenance of system productivity. UMES and DSU faculty and students will be trained in pathogens detection, soilless system quality controls, thus gaining real-world experiences in food safety, microbiome research, and safe food production. UMES and DSU will develop/deliver e-learning workshops on pre-/post-harvest produce safety for students in preparation for professional post-graduate positions. Delmarva growers will be trained in best management practices, and pre-/post-harvest food safety preventive controls for soilless systems. Training/technical assistance in Good Agricultural Practices, Good Handling Practices, and food safety plans will be provided to limited-resource produce growers/market managers serving 1890 institution communities. Thus, this project will build capacity at UMES and DSU in safe soilless LGs production.
Animal Health Component
40%
Research Effort Categories
Basic
20%
Applied
40%
Developmental
40%
Goals / Objectives
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.
Project Methods
The methods proposed to achieve the objectives of this project are summarized as follows:Objective 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 biosensorsby 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 analytics27, as we have demonstrated in other projects.Objective 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. Objective 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.