ENHANCING FOOD SAFETY: RAPID DETECTION OF SALMONELLA IN ONIONS USING MICROSCOPIC IMAGING AND ARTIFICIAL INTELLIGENCE

The major goal of this project is to develop and implement an innovative artificial intelligence (AI) system for rapid detection of Salmonella contamination in onions using microscopic imaging. This project aims to enhance food safety measures by enabling quick, accurate, and cost-effective identification of foodborne pathogens, specifically targeting Salmonella in onion samples. The overarching purpose is to significantly reduce the time required for pathogen detection compared to traditional culture-based methods, thereby improving food safety protocols and potentially preventing foodborne illness outbreaks.To achieve this goal, the project has the following specific objectives:Construct a large-scale image dataset with labels to capture Salmonella Thompson cells in a model food system. This objective involves: a) Cultivating Salmonella Thompson and E. coli K-12 strains under various conditions. b) Preparing and imaging onion samples inoculated with these bacterial strains. c) Capturing high-resolution microscopic images of bacterial microcolonies at different growth stages. d) Annotating and categorizing the images to create a comprehensive dataset for AI model training. Attainability: This objective is achievable within the first year of the project, utilizing 0.7 FTE of the post-doctoral researcher (1.0 FTE in Year 1) and 0.022 FTE of the faculty member. The Olympus IX70 Inverted Phase Contrast DIC Fluorescence Microscope and Seward Stomacher 400 Circulator Blender will be crucial for sample preparation and imaging. The team aims to collect and annotate approximately 1,800 images (1,400 Salmonella, 400 E. coli) within this timeframe. Administrative support (0.05 FTE) will assist with procurement and scheduling. Part of PI's effort 0.022 FTE includes overseeing dataset creation and team coordination. Departmental support (0.05 FTE) will assist with procurement of supplies and scheduling of imaging sessions.Develop real-time, efficient, and automated deep learning models for early detection ofSalmonella in foods. This objective includes: a) Designing and implementing a CNNarchitecture, such as YOLOv4, for bacterial microcolony detection and classification. b) Training the AI model using the curated image dataset to recognize and differentiate Salmonella from other bacterial species. c) Optimizing the model for high accuracy, sensitivity, and specificity in detecting Salmonella contamination. d) Validating the model's performance against standard laboratory methods for Salmonella detection.Attainability: This objective will be primarily addressed in Year 2, utilizing 0.025FTE of the faculty member and 0.3 FTE of the graduate student. The high-performance computing cluster at the university will support the intensive computational requirements for AI model development and training. The team plans to develop and optimize the CNN model within approximately 6-8 months.Administrative support (.025 out of0.05 FTE) will assist with data management and reporting.Part of PI's effort 0.025 FTE in Year 2 includes supervising model development and validation. Departmental support (0.025 FTE (half of the 0.05 FTE allocated)) will assist with data management and progress reporting.Conduct educational transfer activities to promote the incorporation of AI in food science materials and extension programs. This objective involves: a) Organizing and delivering workshops on AI applications in food safety for students, faculty, and industry professionals. b) Developing training materials on microscopic imaging techniques, data curation, and AI model development for food safety applications. c) Providing hands-on experience with the developed AI system to participants. d) Assessing the effectiveness of the educational programs through pre- and post-workshop evaluations.Attainability: This objective will be spread across both years, using approximately 0.0125 FTE out of 0.05 FTE of the faculty member each year. Two workshops are planned: one in the latter part of Year 1 and another in Year 2. The post-doctoral researcher (0.1 FTE) will assist in developing materials in Year 1, while the graduate student (0.1 FTE) will help with the Year 2 workshop. The team aims to train 5-10 participants over the training sessions.Departmental Administrative support (.025 out of0.05 FTE) will handle logistics and participant coordination.Disseminate these technologies to relevant food industry stakeholders and agencies to encourage the integration of AI-enabled imaging sensors for predictive food safety monitoring and smart decision making. This objective includes: a) Presenting research findings at relevant scientific conferences and industry events. b) Publishing results in peer-reviewed journals and open-access platforms. c) Creating a dedicated webpage to showcase the project's progress and outcomes. d) Engaging with food safety regulators and industry leaders to promote adoption of the developed technology.Attainability: This objective will be ongoing throughout the project, intensifying in the second year. It will utilize approximately 0.0125 FTE out of 0.05FTE of the faculty member each year. The team plans to submit at least one peer-reviewed publication and present at one major conference. The post-doc and graduate students (0.1 FTE) will assist in publications.DepartmentalAdministrative supportwill assist with travel arrangements, publication submissions, and stakeholder communication.